text:
abstract={The paper discusses the feasibility of using smart phone devices for human activity registration and analysis. The functional characteristics of the smart phones and their permanent connectivity allow them to serve as a measurement lab and processing unit. An example of using the smart phones as a sensor data source is described, and the corresponding algorithm and results are given. The possible problems are listed and commented.},
author_keywords={Human activity registration;  Smart phone},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Cai2015,
author={Cai, Z.a  b  and Chen, M.a  b },
title={Application of data fusion technology based on D-S evidence theory in fire detection},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9794},
doi={10.1117/12.2203235},
art_number={97941O},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963490916&doi=10.1117%2f12.2203235&partnerID=40&md5=f8e66217d662b7ec3ed99df6571f1815},
affiliation={College of Physics and Information Engineering, Quanzhou Normal University, China; Key Laboratory of Functional Materials for Informatics, Fujian Province University, China},
abstract={Judgment and identification based on single fire characteristic parameter information in fire detection is subject to environmental disturbances, and accordingly its detection performance is limited with the increase of false positive rate and false negative rate. The compound fire detector employs information fusion technology to judge and identify multiple fire characteristic parameters in order to improve the reliability and accuracy of fire detection. The D-S evidence theory is applied to the multi-sensor data-fusion: first normalize the data from all sensors to obtain the normalized basic probability function of the fire occurrence; then conduct the fusion processing using the D-S evidence theory; finally give the judgment results. The results show that the method meets the goal of accurate fire signal identification and increases the accuracy of fire alarm, and therefore is simple and effective. © 2015 SPIE.},
author_keywords={D-S evidence theory;  Data fusion;  Fire detection;  Multi-sensor},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Veitch-Michaelis2015107,
author={Veitch-Michaelis, J.a  and Muller, J.-P.a  and Storey, J.b  and Walton, D.a  and Foster, M.b },
title={DATA FUSION of LIDAR INTO A REGION GROWING STEREO ALGORITHM},
journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives},
year={2015},
volume={40},
number={4W5},
pages={107-112},
doi={10.5194/isprsarchives-XL-4-W5-107-2015},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933045671&doi=10.5194%2fisprsarchives-XL-4-W5-107-2015&partnerID=40&md5=6263bcba08641f2487134d8f87125125},
affiliation={Imaging Group, Mullard Space Science Laboratory, University College London, Holmbury St Mary, United Kingdom; IS Instruments Ltd, 220 Vale Road, Tonbridge, United Kingdom},
abstract={Stereo vision and LIDAR continue to dominate standoff 3D measurement techniques in photogrammetry although the two techniques are normally used in competition. Stereo matching algorithms generate dense 3D data, but perform poorly on low-texture image features. LIDAR measurements are accurate, but imaging requires scanning and produces sparse point clouds. Clearly the two techniques are complementary, but recent attempts to improve stereo matching performance on low-texture surfaces using data fusion have focused on the use of time-of-flight cameras, with comparatively little work involving LIDAR. A low-level data fusion method is shown, involving a scanning LIDAR system and a stereo camera pair. By directly imaging the LIDAR laser spot during a scan, unique stereo correspondences are obtained. These correspondences are used to seed a regiongrowing stereo matcher until the whole image is matched. The iterative nature of the acquisition process minimises the number of LIDAR points needed. This method also enables simple calibration of stereo cameras without the need for targets and trivial coregistration between the stereo and LIDAR point clouds. Examples of this data fusion technique are provided for a variety of scenes. © 2015, Copernicus. All rights reserved.},
author_keywords={3D reconstruction;  calibration;  LIDAR;  Multisensor data fusion;  Stereo vision},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gurken2015838,
author={Gurken, D.a  and Baflio, S.b },
title={Special Section on Sensor Applications},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2015},
volume={64},
number={4},
pages={838-839},
doi={10.1109/TIM.2015.2403711},
art_number={7055960},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924872171&doi=10.1109%2fTIM.2015.2403711&partnerID=40&md5=31865f45647daf0d4bcaa803315d586a},
affiliation={University of Houston, United States; Department of Electronic Instrumentation and Measurements, University of Catania, Italy},
abstract={Sensor applications are one of the emerging topics in the worldwide frontiers of technologies such as smart city, better society, energy, health, environment, emergency management, and industry. The IEEE Sensor Applications Symposium (SAS) has been a leading forum to present novel approaches and emerging applications in sensor technology, including Internet of things, biosensors and arrays, smart sensors and standards, sensor networking, MEMS and nanosensors, virtual sensors, homeland security, multisensor data fusion, nondestructive evaluation and remote sensing, robotics and automation, electromagnetic sensors, computer vision, and commercial development. This special section on the ninth edition, SAS 2014, includes four papers from the symposium that illustrate the diversity of sensor applications emerging in technology and research. © 1963-2012 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kira2015,
author={Kira, Z. and Wagner, A.R. and Kennedy, C. and Zutty, J. and Tuell, G.},
title={STAC: A comprehensive sensor fusion model for scene characterization},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9498},
doi={10.1117/12.2178494},
art_number={949804},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938885013&doi=10.1117%2f12.2178494&partnerID=40&md5=f1c8d12cdcfa09c750c63cfa4da3b922},
affiliation={Georgia Tech Research Institute, 250 14th Street, Atlanta, GA, United States},
abstract={We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models. © 2015 SPIE.},
author_keywords={data fusion;  multisensor fusion;  scene characterization;  scene understanding},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Jafari2015269,
author={Jafari, H. and Poshtan, J.},
title={Fault isolation and diagnosis of induction motor based on multi-sensor data fusion},
journal={6th Annual International Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2015},
year={2015},
pages={269-274},
doi={10.1109/PEDSTC.2015.7093286},
art_number={7093286},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931083789&doi=10.1109%2fPEDSTC.2015.7093286&partnerID=40&md5=3ac19129d143eafe709d87fb0cdd3222},
affiliation={Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran},
abstract={Electric motors of alternating current (AC) are widely used in different industrial applications, and this makes their fault detection very important. One of the most usual induction motor faults is the stator winding inter-turn short circuit. This paper presents a data fusion approach for stator winding fault diagnosis in induction motors using fuzzy measure and fuzzy integral theory. Features are extracted from motor stator current signals, and a technique is used to select some appropriate features from total features. The fuzzy c-mean analysis method is employed to classify induction motor different modes. It is used to option the membership values of each feature group of classes. Different features are fused at feature level using fuzzy measure and fuzzy integral data fusion technique to produce diagnostic results. Results show that the proposed approach performs very well for fault diagnosis of a 4hp laboratory induction motor. © 2015 IEEE.},
author_keywords={data fusion;  fault diagnosis;  fuzzy measure and fuzzy integral theory;  induction motor;  Stator current},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kushwah201519,
author={Kushwah, A. and Kumar, S. and Hegde, R.M.},
title={Multi-sensor data fusion methods for indoor activity recognition using temporal evidence theory},
journal={Pervasive and Mobile Computing},
year={2015},
volume={21},
pages={19-29},
doi={10.1016/j.pmcj.2014.10.009},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937519776&doi=10.1016%2fj.pmcj.2014.10.009&partnerID=40&md5=a4a1e0e1874821b5a7e75999b5ddc5bd},
affiliation={Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India},
abstract={Information fusion has been widely used in context aware applications to create situational awareness. In this paper, a multi sensor fusion methodology using temporal evidence theory is proposed for indoor activity recognition. The fusion method develops an incremental conflict resolution method within the Dempster-Shafer (D-S) theory framework. This method has distinct advantages over the proportional conflict resolution technique of D-S theory. The key contribution of this paper lies in introduction of temporal information into the fusion methodology in a multi-sensor environment. The proposed framework is used for activity detection in smart homes. Two smart home datasets are used in the experiments on activity recognition wherein the data is recorded through a series of passive sensors. The experimental results obtained for activity recognition are motivating enough to be useful in applications like assisted living. © 2014 Elsevier B.V.},
author_keywords={Indoor activity recognition;  Information fusion;  Temporal evidence theory},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou201594,
author={Zhou, F.a  and Zheng, W.a  and Wang, Z.b },
title={Real-time motion estimation for UAVs based on dissimilar multi-sensor data fusion},
journal={Jiqiren/Robot},
year={2015},
volume={37},
number={1},
pages={94-101},
doi={10.13973/j.cnki.robot.2015.0094},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929247853&doi=10.13973%2fj.cnki.robot.2015.0094&partnerID=40&md5=33dd648ec88af086fbffad2fb3dc6304},
affiliation={Department of Automation, University of Science and Technology of China, Hefei, China; Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China},
abstract={Large time-delay usually exists in the visual localization results of unmanned aerial vehicles (UAVs), which lowers the accuracy of motion estimation. To solve this problem, a real-time motion estimation method based on multisensor data fusion is proposed. Firstly, attitude information provided by the inertial measurement unit (IMU) is fused into the monocular visual localization algorithm, so that the delay in localization results can be reduced. Then, the delay of the visual localization results is considered during the motion estimation using Kalman filter, and the measurements of the accelerometer are used to compensate the delay. With the proposed method, motion estimation results with high accuracy are obtained in real time. The method is validated by field experiments on a quadrotor system. By comparing the motion estimation results with the method without delay compensation as well as the ground truth data, the effectiveness of the proposed method is verified. ©, 2015, Chinese Academy of Sciences. All right reserved.},
author_keywords={Delay compensation;  Sensors data fusion;  UAV (unmanned aerial vehicle);  Visual navigation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Oh2015133,
author={Oh, Y.},
title={Context awareness of smart space for life safety},
journal={International Journal of Smart Home},
year={2015},
volume={9},
number={1},
pages={133-140},
doi={10.14257/ijsh.2015.9.1.14},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922444405&doi=10.14257%2fijsh.2015.9.1.14&partnerID=40&md5=35abeca270fa77ea5fbb7145e8244300},
affiliation={Department of Information and Communication, Korea Nazarene University, South Korea},
abstract={This study aimed at preventing crimes and various accidents. The space of residential environment provides service of preventing and avoiding various risks by demonstrating the function of being aware of various risks in advance. This study proposed a plan that guaranteed concealment in the environment which used cheap small sensor and enabled advance awareness even for a risk which deviated the range of advance information-based data modeling. Risk context reasoning was made to become clear through repeatedly checking a risk factor to be identified, as a multi-sensor data fusion-based risk awareness scheme which used DST. © 2015 SERSC.},
author_keywords={Context awareness;  Data fusion;  Multi-sensor;  Stream data mining},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hu20151011,
author={Hu, G.a  and Gao, S.a  and Zhong, Y.b  and Gao, B.a  and Subic, A.c },
title={Matrix weighted multisensor data fusion for INS/GNSS/CNS integration},
journal={Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering},
year={2015},
volume={230},
number={6},
pages={1011-1026},
doi={10.1177/0954410015602723},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964573616&doi=10.1177%2f0954410015602723&partnerID=40&md5=5de5e3d8fd70524e254da75a091e1d48},
affiliation={School of Automatics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, China; School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Bundoora, Australia; Swinburne Research and Development, Swinburne University of Technology, Hawthorn, Australia},
abstract={Inertial navigation system (INS)/global navigation satellite system (GNSS)/ celestial navigation system (CNS) integration is a promising solution to improve the performance of navigation due to the complementary characteristics of INS, GNSS, and CNS. Nevertheless, the information fusion involved in INS/GNSS/CNS integration is still an open issue. This paper presents a matrix weighted multisensor data fusion methodology with two-level structure for INS/GNSS/CNS integrated navigation system. On the first level, GNSS and CNS are integrated with INS by two local filters respectively to obtain local optimal state estimations. On the second level, two different matrix weighted data fusion algorithms, one based on generic weighting matrices and the other based on diagonal weighting matrices, are developed to fuse the local state estimations for generating the global optimal state estimation. These two algorithms are derived in the sense of linear minimum variance, which provide unbiased fusion results no matter whether the local state estimations are mutually independent or not. Thus, they overcome the limitations of the federated Kalman filter by refraining from the use of the upper bound technique. Compared with the data fusion algorithm based on generic weighting matrices, the computational load involved in the one based on diagonal weighting matrices is significantly reduced, even though its accuracy is slightly lower due to the disregard of the coupled relationship between the components of the local state estimations. The effectiveness of the proposed matrix weighted multisensor data fusion methodology is verified through Monte Carlo simulations and practical experiments in comparison with the federated Kalman filter. © 2015 Institution of Mechanical Engineers.},
author_keywords={data fusion;  INS/GNSS/CNS integration;  Kalman filtering;  multisensor system;  weighting matrix},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Jeong2015,
author={Jeong, N.-K.a  and Jung, H.-S.a  and Park, S.-H.a  and Oh, K.-Y.a  b },
title={Multi-sensor data fusion of VNIR and TIR satellite imagery},
journal={ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings},
year={2015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964056055&partnerID=40&md5=f22f136b7af007217c0c5c300a2cac30},
affiliation={University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, South Korea; Korea Environment Institute, 370 Sicheong-daero, Sejong, South Korea},
abstract={In this paper, we study the data fusion of visible and near infrared (VNIR) and thermal infrared (TIR) imagery. VNIR image gives high spatial resolution and clear edge expression information while TIR image gives Earth's surface temperature information. Frequently, it is not easy to utilize the TIR images in many remote sensing applications, because the TIR images does not have a good spatial resolution enough to describe spatial details. Thus, in order to improve the TIR image's availability and utilization, a multi-sensor data fusion of TIR and VNIR is quite necessary. In this paper, we have proposed an efficient way to fuse the VNIR and TIR images of Landsat 5 TM and ASTER. The panchromatic images is generated by using principal component analysis (PCA) from multi-spectral images, and the fused image is created by merging the panchromatic and thermal images. And finally, the performance test of the proposed fusion method is performed by using entropy concept. The method can be applied to detect hot-spot area such as forest fire and investigate a nuclear test and a surface temperature change in urban area.},
author_keywords={Data fusion;  Entropy;  Thermal infrared(TIR);  Visible and near infrared(VNIR)},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hannachi2015,
author={Hannachi, A.a  and Kohler, S.b  and Lallement, A.a  and Hirsch, E.a },
title={Multi-sensor data fusion for realistic and accurate 3d reconstruction},
journal={EUVIP 2014 - 5th European Workshop on Visual Information Processing},
year={2015},
doi={10.1109/EUVIP.2014.7018381},
art_number={7018381},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923609366&doi=10.1109%2fEUVIP.2014.7018381&partnerID=40&md5=727f8b664aa11eb4932864e98d508bed},
affiliation={ICUBE, Laboratoire des Sciences de l'Ingénieur, UMR 7357 CNRS-UdS, Boulevard Sébastien Brand, CS 10413, Illkirch cedex, France; MIPS, Laboratoire Modélisation Intelligence Processus Systèmes, Université de Haute Alsace, 12 rue des frères Lumière, Mulhouse Cedex, France},
abstract={In this paper, the problem of multi-sensor data fusion for 3D dimensional reconstruction/assessment of manufactured parts is investigated. For this, a method relying on cooperation of two acquisition/processing techniques (stereoscopic and structured light sensors) is presented. The stereoscopic system reconstructs the outline of the object, whereas the structured light system represents the workpiece as points that characterize its 3D surfaces. The developed approach exploits the complementarity of the two acquired data sets. To represent the whole set of measurements in a common reference frame, a fusion method is developed based on the geometric characteristics of the workpiece used to define a cost function, which allows perfect alignment of the two point sets. This is further favored by use of a camera common to the two sensory systems. Experimental results demonstrate that the proposed method outputs results, which can be meaningfully used in applications such as quality control. © 2014 IEEE.},
author_keywords={geometric cost function;  multi-sensor data fusion;  optimization;  stereo system;  Structured light},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Prado2015186,
author={Prado, J. and Marques, L.},
title={Multi-sensor and multi-platform data fusion for buried objects detection and localization},
journal={Proceedings - 2015 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2015},
year={2015},
pages={186-191},
doi={10.1109/ICARSC.2015.29},
art_number={7101631},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933073464&doi=10.1109%2fICARSC.2015.29&partnerID=40&md5=1bc548967a747806ccd80c30cdb55998},
affiliation={Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal},
abstract={This paper describes a multi-sensor data-fusion approach to detect and localize landmines and unexploded ordnances (UXO) in a field using multiple mobile sensor carriers covering the area of interest. A procedure for high accuracy geo-referencing the field-data acquired by multiple platforms is proposed. Later, the geo-referenced sensor data is fused by a multi-step algorithm consisting on the detection of heterogeneities corresponding to objects followed by the classification of those objects through a decision-level fusion. The proposed approach was tested and validated in a military field containing more than twenty different objects buried on the ground, using two different metal detector (MD) arrays and one ground penetrating radar (GPR) array. Although the preliminary results can be considered encouraging, since it was possible to detect and classify all the buried objects, further tests are necessary in terms of classification, since the classification algorithm was trained with data collected previously from the same soil. © 2015 IEEE.},
author_keywords={Bayesian classification;  Field robotics;  Multi-sensor data fusion;  Robotics demining},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Marinushkin2015,
author={Marinushkin, P.S. and Podshivalov, I.A.},
title={MEMS-based non-orthogonal redundant inertial measurement unit for miniature navigation systems},
journal={2015 International Siberian Conference on Control and Communications, SIBCON 2015 - Proceedings},
year={2015},
doi={10.1109/SIBCON.2015.7147138},
art_number={7147138},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941048131&doi=10.1109%2fSIBCON.2015.7147138&partnerID=40&md5=92883fcdc73bdc90c150a29004988acd},
affiliation={Institute of Engineering Physics and Radioelectronics, Siberian Federal University, Krasnoyarsk, Russian Federation},
abstract={This paper describes the features of designing a MEMS-based non-orthogonal redundant inertial measurement unit for miniature navigation systems. With the aim of increasing the reliability and accuracy of motion parameters estimation considered, the inertial measurement unit in the form of a truncated regular tetragonal pyramid, at the sides of which inertial MEMS sensors (accelerometers and gyroscopes) are located, was considered. The performance of the considered configuration is analyzed and the corresponding multisensor data fusion algorithm is provided. The results of the preliminary experiments are also given. © 2015 IEEE.},
author_keywords={inertial navigation;  microelectromechanical systems (MEMS);  pedestrian navigation;  redundancy;  strapdown inertial navigation systems},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Shi2015252,
author={Shi, Y. and Zhou, X.},
title={Nonlinear sequential fusion on direct filtering and information filtering based on the CKF},
journal={ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control},
year={2015},
pages={252-257},
doi={10.1109/ICNSC.2015.7116044},
art_number={7116044},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941217313&doi=10.1109%2fICNSC.2015.7116044&partnerID=40&md5=27a504e3dc557683cfa962550aa809f2},
affiliation={School of Economics and Management, Shanghai Maritime University, Shanghai, China},
abstract={Nonlinear filtering is a lasting and challenging topic in information fusion filed all along. Due to the complexity of nonlinear systems, it makes that data fusion based on nonlinear filtering distinctly differs from the traditional linear data fusion. For the linear Kalman fusion, there are some important properties, for example, equivalence of the Kalman filter and its information filtering form and equivalence between sequential fusion algorithms based on the direct Kalman filtering and the information filtering. However, we cannot directly and subjectively ensure that all of conclusions or properties in linear fusion can be followed for nonlinear systems because performance of nonlinear fusion depends closely on the concrete nonlinear filters. For these, we study two equivalent problems on estimation fusion accuracies. The first is estimation equivalence of the direct cubature Kalman filter and its associated information filter and the second is to theoretically prove fusion accuracy equivalence of two multisensor sequential fusion methods based on the direct cubature Kalman filter and the information filter. The results on theoretical proof and simulation examples show explicitly and clearly that the two equivalence properties existed in the current linear Kalman estimation fusion can be completely succeeded in the cubature Kalman estimation fusion for nonlinear systems. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{André2015166,
author={André, C. and Hégarat-Mascle, S.L. and Reynaud, R.},
title={Evidential framework for data fusion in a multi-sensor surveillance system},
journal={Engineering Applications of Artificial Intelligence},
year={2015},
volume={43},
pages={166-180},
doi={10.1016/j.engappai.2015.04.012},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929875170&doi=10.1016%2fj.engappai.2015.04.012&partnerID=40&md5=bfd9e5f396de353a2df3eeadf240abc0},
affiliation={Institute of Fundamental Electronics, University Paris-Sud, cedex Orsay, France},
abstract={The multi-sensor data fusion relies on a combination of information pieces to produce a more accurate or complete description of the environment. In this work, we considered the case of a surveillance system using several heterogeneous sensors in a network. In such a system, the data fusion objective is to merge the detections provided by the different sensors in order to count, locate and track all the targets in the monitored area. The problem was addressed in the context of Belief Function theory in order to cope with the high inaccuracy of information and the different forms of imprecision. In this framework, we developed a unified approach to model and merge the detections coming from various kinds of sensors with prior knowledge about target location derived from topographical elements. We showed that the developed belief model provided an efficient measurement for data association between tracks and detections. Considering scalable constraints for the system, the complexity and consistency of belief function representation should be controlled, which was achieved by implementing versatile discernment frames and by restricting the number of focal elements. The proof of concept of the proposed data fusion module was achieved by implementing it in an actual detection system. Real-world scenarios were used to draw some conclusions about localization performance and end-user perception. Further experiments were also performed on simulated data to focus on data association and belief function simplification subproblems. © 2015 Elsevier Ltd.},
author_keywords={Belief Function approximation;  Belief Function Theory;  Evidential objects/tracks association;  Surveillance system},
document_type={Article},
source={Scopus},
}

@ARTICLE{Fortino201550,
author={Fortino, G.a  and Galzarano, S.a  and Gravina, R.a  and Li, W.b },
title={A framework for collaborative computing and multi-sensor data fusion in body sensor networks},
journal={Information Fusion},
year={2015},
volume={22},
pages={50-70},
doi={10.1016/j.inffus.2014.03.005},
note={cited By 47},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907599702&doi=10.1016%2fj.inffus.2014.03.005&partnerID=40&md5=0389d807db2cbe285b26f2021985e202},
affiliation={Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, Via P. Bucci, 87036 Rende, CS, Italy; School of Logistics Engineering, Wuhan University of Technology, 430063 Wuhan, China},
abstract={Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multi-sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals' meeting. © 2014 Elsevier B.V. All rights reserved.},
author_keywords={Body sensor networks;  Collaborative computing;  Emotion detection;  Handshake detection;  Multi-sensor data fusion;  SPINE},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jia2015142,
author={Jia, Y. and Hu, X.},
title={Virtual axis machine tool positioning method},
journal={Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis},
year={2015},
volume={35},
number={1},
pages={142-145},
doi={10.16450/j.cnki.issn.1004-6801.2015.01.024},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928232733&doi=10.16450%2fj.cnki.issn.1004-6801.2015.01.024&partnerID=40&md5=2710cc760f8be2e2a2ce15903abab769},
affiliation={School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China},
abstract={To solve the dynamic virtual axis machine tool spindle positioning on the platform, a method is presented based on the improved consistency of multi-sensor data fusion virtual axis machine tool positioning, visual technology and electronic compass used in the measurement of the position and orientation of the virtual axis machine tool moving platform. This method is the virtual axis machine tools of each rod of the encoder information, visual information and the dynamic inertial measurement data fusion, obtained by dynamic calculation of the confidence distance relationship matrix, the position and attitude of the virtual axis machine tool moving platform (pose) the precise positioning of a good guarantee. After a simplified test of the direction of movement of a single axle measurement, the measurement of the virtual axis machine tool 6 degrees of freedom of the moving platform pose as the object of study, and its simulation test. The test results show that the proposed positioning method is feasible, effective, and practically significant. ©, 2015, Nanjing University of Aeronautics an Astronautics. All right reserved.},
author_keywords={Data fusion;  Locating;  Multi-sensor;  Virtual axis machine tool},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Quaranta2015,
author={Quaranta, C. and Balzarotti, G.},
title={A technique for sensors fusion with limited number of common measures},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9474},
doi={10.1117/12.2177069},
art_number={94740O},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946068476&doi=10.1117%2f12.2177069&partnerID=40&md5=528864b3769b0041ae1ee7a5405a61d8},
affiliation={Selex ES, Radar and Advanced Targeting LoB, IRST Skyward, Viale Europa, Nerviano (MI), Italy},
abstract={An algorithm is proposed here that overcomes the problem of the not exhaustive number of common measures from sensors of different kind. In the presence of a suite of heterogeneous sensors, the data fusion process has to deal with the problem of managing different information, generally not directly comparable. The analysis of the mathematical model is carried out considering a data fusion system between radar and Infrared Search and Track (IRST) where the measurement of the range is achieved by radar only. Simulation results demonstrate the effectiveness of the algorithm as regards the fusion process, tracking and correctness of association among tracks from different sensors. A comparison with a known approach from the literature about the fusion equation is also performed. © 2015 SPIE.},
author_keywords={Data fusion;  IMM;  IRST;  Multisensor target tracking;  RADAR},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gerardo-Castro2015289,
author={Gerardo-Castro, M.P.a  and Peynot, T.a  and Ramos, F.b },
title={Laser-radar data fusion with Gaussian process implicit surfaces},
journal={Springer Tracts in Advanced Robotics},
year={2015},
volume={105},
pages={289-302},
doi={10.1007/978-3-319-07488-7_20},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928230236&doi=10.1007%2f978-3-319-07488-7_20&partnerID=40&md5=b56bc3b088fe87b668258ea5ef76c1c8},
affiliation={Australian Centre for Field Robotics (ACFR), The University of SydneyNSW, Australia; ACFR, School of Information Technologies, The University of SydneyNSW, Australia},
abstract={This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive. © Springer International Publishing Switzerland 2015.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Shen201593,
author={Shen, J.a  b  and Zhang, Z.a  and Bai, H.c  and Liu, S.d  and Gu, H.a  and Wang, H.a },
title={An AUV integrated navigation method based on multi-sensor data fusion},
journal={International Journal of Control and Automation},
year={2015},
volume={8},
number={1},
pages={93-102},
doi={10.14257/ijca.2015.8.1.08},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922334256&doi=10.14257%2fijca.2015.8.1.08&partnerID=40&md5=067db6939c590e792cb70b226a279b27},
affiliation={College of Computer and Information Engineering, Hohai University, Nanjing, China; PLA University of Science and Technology, Nanjing, China; School of energy and power engineering, Nanjing University of Science and Technology, Nanjing, China; School of physical science and technology, Heilongjiang University, Haerbin, China},
abstract={Autonomous Navigation Vehicle (AUV) navigates to the specified longitude and latitude on water surface by means of Global Positioning System (GPS), Inertial Navigation System (INS) and Electronic Compass (EC), then dives to the specified depth according to the measurements of Depth Gauge (DG). To overcome the low location accuracy of existing navigation technologies in AUV, an integrated navigation method based on multi-sensor data fusion of GPS/INS/EC/DG has been proposed. Since the systematic stability and high precision in short-term measurement of INS, it takes the INS as common reference system. The problem which measurement error accumulates with time is solved by the combination of four sub-filters and a main filter. The sub-filters output the measured velocity, position, orientation and depth error to the main filter, which fuse the data to obtain the global optimal estimation and feedback to the INS system. Realistic environment parameters are simulated in MATLAB to verify the rationality of the navigation algorithm. The feasibility of method is proved by the comparison of position errors with and without combined navigation. © 2015 SERSC.},
author_keywords={Autonomous navigation;  AUV;  Error correction;  Integrated navigation;  Kalman filtering},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Demars20155212,
author={Demars, K.J. and McCabe, J.S. and Darling, J.E.},
title={Efficient multi-sensor data fusion for space surveillance},
journal={Proceedings of the American Control Conference},
year={2015},
volume={2015-July},
pages={5212-5217},
doi={10.1109/ACC.2015.7172153},
art_number={7172153},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940947504&doi=10.1109%2fACC.2015.7172153&partnerID=40&md5=ca16ef41bc335c627352a39256d34ae9},
affiliation={Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, United States},
abstract={Multi-sensor networks can extend the sensing region of a single sensor in order to provide a more geometrically diverse and comprehensive view of the state of a dynamical system. The use of a multi-sensor network gives rise to the need for a fusion step that combines the outputs of all sensor nodes into a single probabilistic state description. This paper examines a fusion method based on logarithmic opinion pools and develops algorithms for multi-sensor data fusion as well as investigates weight selection schemes for the opinion pool using efficient quadrature integration methods. The proposed fusion rules are applied to the tracking of a space object using multiple ground-based optical sensors. It is shown that the multi-sensor fusion rule leads to an increase of nearly two orders of magnitude in the position tracking accuracy as compared to the traditional single-sensor tracking method. © 2015 American Automatic Control Council.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gui20151394,
author={Gui, Z.-W.a  and Wu, T.a  and Peng, X.b },
title={A novel recognition approach for mobile image fusing inertial sensors},
journal={Zidonghua Xuebao/Acta Automatica Sinica},
year={2015},
volume={41},
number={8},
pages={1394-1404},
doi={10.16383/j.aas.2015.c140177},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940743365&doi=10.16383%2fj.aas.2015.c140177&partnerID=40&md5=3332a11fc57d82e0bf892e839499d119},
affiliation={China Electronics Technology Group Corporation No. 7 Research Institute, Guangzhou, China; Guangzhou Jie Sai Science and Technology Co. Ltd., Guangzhou, China},
abstract={Multi-sensor data fusion as a special means of data processing in the field of image recognition has been developed rapidly. This paper presents a novel recognition approach for mobile image to fuse multi-sensor data. Firstly, the local features are extracted by fusing the sensor information to enhance the distinguish ability of them; secondly, the established method of random clustering forest is improved to reduce the training time of sample images; finally, the fast geometric consistency approach is used to check the matching result to ensure the recognition accuracy. Experimental results show that the proposed method can quickly and efficiently recognize the object and has strong robustness. It also has a higher accuracy, faster recognition speed, and less training complexity than the traditional method of vocabulary tree. Copyright © 2015 Acta Autornatica. All rights reserved.},
author_keywords={Mobile image recognition;  Multi-sensor data fusion;  Randomized clustering forests;  Smartphone},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2015334,
author={Wang, P.},
title={Trajectory algorithm for underwater target based on Kalman filter},
journal={Metallurgical and Mining Industry},
year={2015},
volume={7},
number={7},
pages={334-339},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942313840&partnerID=40&md5=baaab0b8bf64df4ab1bb122e1e67a77f},
affiliation={School of Electronic Information Engineering, Xi'an Technological University, Xi'an, China},
abstract={To improve the accuracy of identification and the damage efficiency for underwater target, this paper researched the processing algorithm of moving trajectory for underwater target. This paper ameliorated the conventional Kalman filter algorithm in order to obtain true moving trajectory of underwater target, adopted state space description method and recursive algorithm to establish the signal's nonlinear state equation for the motion model of target, used the Unscented Kalman Filter (UKF) method to get rid of the influence of noise and then to get more accurate estimated value of the target location. The results of simulation calculation from multi-sensor data fusion show the real-time tracking data had high accuracy, and then the tracking ability was very powerful. Multi-sensor data fusion can effectively reduce the error and achieve the purpose of rapid convergence. The results of simulation calculation verify the correctness and effectiveness of this method.},
author_keywords={Kalman filter;  Moving trajectory;  Underwater target},
document_type={Article},
source={Scopus},
}

@ARTICLE{Colosimo2015199,
author={Colosimo, B.M.a  and Pacella, M.b  and Senin, N.c },
title={Multisensor data fusion via Gaussian process models for dimensional and geometric verification},
journal={Precision Engineering},
year={2015},
volume={40},
pages={199-213},
doi={10.1016/j.precisioneng.2014.11.011},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923065239&doi=10.1016%2fj.precisioneng.2014.11.011&partnerID=40&md5=9b830d1a04ffcc3a0ef850451747b0b5},
affiliation={Politecnico di Milano, Dip. di Meccanica, via G. La Masa 1, Milano, Italy; Università Del Salento, Dip. di Ingegneria dell'Innovazione, via per Monteroni, Lecce, Italy; Università di Perugia, Dip. di Ingegneria, via G. Duranti 67, Perugia, Italy},
abstract={An increasing amount of commercial measurement instruments implementing a wide range of measurement technologies is rapidly becoming available for dimensional and geometric verification. Multiple solutions are often acquired within the shop-floor with the aim of providing alternatives to cover a wider array of measurement needs, thus overcoming the limitations of individual instruments and technologies. In such scenarios, multisensor data fusion aims at going one step further by seeking original and different ways to analyze and combine multiple measurement datasets taken from the same measurand, in order to produce synergistic effects and ultimately obtain overall better measurement results. In this work an original approach to multisensor data fusion is presented, based on the development of Gaussian process models (the technique also known as kriging), starting from point sets acquired from multiple instruments. The approach is illustrated and validated through the application to a simulated test case and two real-life industrial metrology scenarios involving structured light scanners and coordinate measurement machines. The results show that not only the proposed approach allows for obtaining final measurement results whose metrological quality transcends that of the original single-sensor datasets, but also it allows to better characterize metrological performance and potential sources of measurement error originated from within each individual sensor. © 2014 Elsevier Inc. All rights reserved.},
author_keywords={CMM;  Coordinate metrology;  Gaussian process;  Kriging;  Multisensor data fusion;  Structured light scanner},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Minor2015,
author={Minor, C.a  and Johnson, K.b },
title={Reliable sources and uncertain decisions in multisensor systems},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9498},
doi={10.1117/12.2177389},
art_number={949803},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938842968&doi=10.1117%2f12.2177389&partnerID=40&md5=f70e75d2fa9ea32344bf03a344455993},
affiliation={Nova Research Inc., Alexandria, VA, United States; Naval Research Laboratory, Washington, DC, United States},
abstract={Conflict among information sources is a feature of fused multisource and multisensor systems. Accordingly, the subject of conflict resolution has a long history in the literature of data fusion algorithms such as that of Dempster-Shafer theory (DS). Most conflict resolution strategies focus on distributing the conflict among the elements of the frame of discernment (the set of hypotheses that describe the possible decisions for which evidence is obtained) through rescaling of the evidence. These "closed-world" strategies imply that conflict is due to the uncertainty in evidence sources stemming from their reliability. An alternative approach is the "open-world" hypothesis, which allows for the presence of «unknown» elements not included in the original frame of discernment. Here, conflict must be considered as a result of uncertainty in the frame of the discernment, rather than solely the province of evidence sources. Uncertainty in the operating environment of a fused system is likely to appear as an open-world scenario. Understanding the origin of conflict (source versus frame of discernment uncertainty) is a challenging area for research in fused systems. Determining the ratio of these uncertainties provides useful insights into the operation of fused systems and confidence in their decisions for a variety of operating environments. Results and discussion for the computation of these uncertainties are presented for several combination rules with simulated data sets. © 2015 SPIE.},
author_keywords={combination rules;  data fusion;  Dempster-Shafer theory;  interferants;  multisensor systems;  open-world sensing;  sensor reliability},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Obst2015143,
author={Obst, M.a  and Hobert, L.b  and Reisdorf, P.c },
title={Multi-sensor data fusion for checking plausibility of V2V communications by vision-based multiple-object tracking},
journal={IEEE Vehicular Networking Conference, VNC},
year={2015},
volume={2015-January},
number={January},
pages={143-150},
doi={10.1109/VNC.2014.7013333},
art_number={7013333},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936871992&doi=10.1109%2fVNC.2014.7013333&partnerID=40&md5=ed7a87f95efe50aa3923deb8d8491cd6},
affiliation={BASELABS GmbH, Ebertstr. 10, Chemnitz, Germany; Hitachi Europe SAS, 955, Route des Lucioles, Sophia Antipolis, France; Technische Universität Chemnitz, Reichenhainer Str. 70, Chemnitz, Germany},
abstract={Automated vehicles require an adequate and reliable perception of the surrounding world in order to make good decisions. Using vehicle-to-vehicle (V2V) communication to exchange location data (i.e. time, position, heading and speed) can improve the perception beyond the capabilities of traditional on-board sensors (e.g. radar, lidar). However, it is vital to trust the data before it is being used. Cryptographic mechanisms can protect the exchange and authenticity of data but do not guarantee the correctness of the content. In this paper we present a vision-based multi-object tracking system for checking the plausibility of V2V communication. The system is addressing the challenge of fusing relative sensor observations as provided by a MobilEye vision-system with time-delayed absolute GNSS-based measurements from Cooperative Awareness Messages (CAMs) as provided by V2V. The plausibility check is implemented in a prototype and based on a state-of-the-art multiple-object tracking algorithm. The proposed system is evaluated and validated under real-world conditions by conducting several test drives under urban conditions. © 2014 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kumar201555,
author={Kumar, S. and Tiwari, S.N. and Hegde, R.M.},
title={Sensor node tracking using semi-supervised Hidden Markov Models},
journal={Ad Hoc Networks},
year={2015},
volume={33},
pages={55-70},
doi={10.1016/j.adhoc.2015.04.004},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941875224&doi=10.1016%2fj.adhoc.2015.04.004&partnerID=40&md5=3d6ca0eb346902c71f335d847fdf08c1},
affiliation={Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India},
abstract={In this paper, a novel method for mobile sensor node tracking using semi-supervised Hidden Markov Models (HMM) is discussed. A new methodology to develop a combined attenuation model from data gathered from multiple sensors is also described. Observations emitted from the nodes are sparsely measured over the network area with beacons placed on the boundaries. HMMs are trained using observations measured at each grid point. The distances between a node passing through a specific grid point and beacons are estimated using likelihood maximization. The local location co-ordinates of the node positions are then computed by solving a constrained volume optimization problem. Quaternion rotation is used to finally obtain global coordinates of the node location. Several standard manoeuvres of mobile nodes are first simulated. Similar manoeuvres are also recorded from real field deployments. The experimental results are obtained for node localization and tracking from these experiments. Results indicate an improvement in the localization accuracy, when compared to the conventional localization methods. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Ad-hoc sensor networks;  Hidden Markov Model (HMM);  Multi-sensor data fusion;  Semi-supervised methods;  Sensor node localization;  Sensor node tracking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Wang2015165,
author={Wang, W.a  and Zhu, Y.a  and Yu, T.b  and Wang, M.a },
title={The application of wireless network on quadrotor temperature observe based on NRF905},
journal={Industrial, Mechanical and Manufacturing Science - Proceedings of the 2014 International Conference on Industrial, Mechanical and Manufacturing Science, ICIMMS 2014},
year={2015},
pages={165-168},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941619552&partnerID=40&md5=310ba517130663e87a32d13a8078bde3},
affiliation={School of Electrical Engineering, Guizhou University, Guiyang, Guizhou, China; Changchun railway vehicle equipment co., LTD, The Guokai company, China},
abstract={In order to solve the problem of quadrotor temperature observe such as temperature compensation of aircraft flying height, difficulties on networking and maintaining as well as the high cost between the upper machine and lower machine, the design has changed the signal transmission into the wireless network approach, thus making the whole system easy to maintain, easy to organize network, and low cost. The use of multi-sensor data fusion technology in alarm system ensures the reliability of the entire flight control system. The classic PID control strategy used in flight control makes the aircraft flying height a better response speed and control precision. Tests show that the entire system can work reliably at the range of 500 meters. © 2015 Taylor & Francis Group, London.},
author_keywords={Data fusion;  Flying height;  NFR905;  PID;  Temperature observe},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Sun2015595,
author={Sun, B.-L. and Jiang, C.-L. and Li, M. and Cheng, X.-Y.},
title={Fuzzy logic-based interacting multiple model algorithm of networked munitions for target tracking},
journal={Binggong Xuebao/Acta Armamentarii},
year={2015},
volume={36},
number={4},
pages={595-601},
doi={10.3969/j.issn.1000-1093.2015.04.004},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930172069&doi=10.3969%2fj.issn.1000-1093.2015.04.004&partnerID=40&md5=da6e55291e31cc498dc07ced0182f4f3},
affiliation={School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China},
abstract={Fuzzy logic-based interacting multiple model (FL-IMM) algorithm in target tracking is proposed for networked munitions. During the interacting stage, the difference value between the theoretical value of covariance matrix and its estimated value is used to adaptively adjust the measured error of the system. In order to fuse multi-nodes data, a FFS (fuzzy-fusion system) is proposed to obtain the target state estimation. The feasibility of the proposed algorithm is verified through a network with 3 detecting nodes. The results show that the algorithm can effectively trace the maneuvering target under the condition of sensor failure and unknown system measurement error, which means that FL-IMM algorithm has great practicability in tracking the targets by the ground networked munition multi-nodes. ©, 2015, China Ordnance Society. All right reserved.},
author_keywords={Fuzzy logic;  Interacting multiple model;  Multi-sensor data fusion;  Networked munitions;  Ordnance science and technology;  Target tracking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li20151121,
author={Li, M.a  b  and Pei, T.a  b  and Zhu, J.a  b },
title={A multi-sensor data fusion algorithm based on adaptive weight for wireless sensor networks},
journal={Journal of Computational Information Systems},
year={2015},
volume={11},
number={3},
pages={1121-1131},
doi={10.12733/jcis13410},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927166180&doi=10.12733%2fjcis13410&partnerID=40&md5=cecd922395a3c3c71b871a2af6eb5725},
affiliation={College of Information Engineering, Xiangtan University, Xiangtan, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China},
abstract={Aiming to accomplish the task of multiple sensor fusion on measurement accuracy for wireless sensor networks, a novel algorithm based on adaptive weight is put forward in this paper. New spatial relative distances and correlation coefficients are defined to obtain valid observation data. Then, a novel consistent matrix is obtained by removing the data containing errors. Finally, a trust degree calculation is used to assign the weight of the valid observation data in the fusion process, as well as increasing the weight of the valid data. This method can make good use of spatial mapping and limit the influence of noise on the fusion stage. Simulation results from this new algorithm demonstrate better performance and higher reliability compared to the other three existing algorithms. ©, 2015, Binary Information Press. All right reserved.},
author_keywords={Correlation coefficient;  Data fusion;  Trust degree;  Variable weight;  Wireless sensor networks},
document_type={Article},
source={Scopus},
}

@ARTICLE{Moser201540,
author={Moser, G.a  and Tuia, D.b  and Shimoni, M.c },
title={2015 IEEE GRSS data fusion contest: Extremely high resolution LiDAR and optical data [Technical committees]},
journal={IEEE Geoscience and Remote Sensing Magazine},
year={2015},
volume={3},
number={1},
pages={40-41},
doi={10.1109/MGRS.2015.2397448},
art_number={7067041},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926442791&doi=10.1109%2fMGRS.2015.2397448&partnerID=40&md5=559d7dbba78d2d3aaa6b6713b3457256},
affiliation={Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy; GIUZ, University of Zurich, Zurich, Switzerland; Electrical Engineering Department, Signal and Image Centre, RMA, Brussels, Belgium},
abstract={The 2015 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), aims at providing a challenging image analysis opportunity, including multiresolution and multisensor fusion at extremely high resolution. The 2015 Contest involves two datasets acquired simultaneously by passive and active sensors. The passive data is a 5cm-resolution color ortho-photo acquired in the visible wavelength range. The active data source is a 65 pts/m2 LiDAR 3D point cloud that is provided both as raw 3D coordinates (ASCII format) and as a Digital Surface Model (DSM) with a point spacing of about 10 cm. The laser scan rate, angle, and frequency were 125 Hz, 20°, and 49 Hz, respectively. The LiDAR data include the first, last and one intermediate return. © 2013 IEEE.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Fok20151904,
author={Fok, K.-Y. and Cheng, C.-T. and Ganganath, N.},
title={Live demonstration: A HMM-based real-time sign language recognition system with multiple depth sensors},
journal={Proceedings - IEEE International Symposium on Circuits and Systems},
year={2015},
volume={2015-July},
pages={1904},
doi={10.1109/ISCAS.2015.7169037},
art_number={7169037},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946208406&doi=10.1109%2fISCAS.2015.7169037&partnerID=40&md5=e07d3e5ff0483f8bc2d44eba0fe9b6a1},
affiliation={Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong},
abstract={Automatic sign language recognition plays an important role in communications for sign language users. Most existing sign language recognition systems use single sensor input. However, such systems may fail to recognize hand gestures correctly due to occluded regions of hand gestures. In this work, we propose a novel system for real-time recognition of the digits in American Sign Language (ASL) [1]. The proposed system [2] utilizes two Leap Motion sensors [3] to capture hand gestures from different angles. Sensory data are preprocessed using a multi-sensor data fusion approach and ASL digits are recognized in real-time from the fused data using Hidden Markov models (HMM) [4]. Experimental results of the proposed sign language recognition system demonstrate its improved performance over single sensor systems. With a low implementation cost and a high recognition accuracy, the proposed system can be widely adopted in many real world applications and bring conveniences to world-wide ASL users. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hernandez20153931,
author={Hernandez, K.a  and Spall, J.C.b  c },
title={System identification for multi-sensor data fusion},
journal={Proceedings of the American Control Conference},
year={2015},
volume={2015-July},
pages={3931-3936},
doi={10.1109/ACC.2015.7171943},
art_number={7171943},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940905015&doi=10.1109%2fACC.2015.7171943&partnerID=40&md5=646980d806f95ce94d0601716f0b2a68},
affiliation={Johns Hopkins University, Department of Applied Mathematics and Statistics, United States; Johns Hopkins University, Applied Physics Laboratory, United States; JHU Department of Applied Mathematics and Statistics, United States},
abstract={In this paper we discuss the problem of combining sensor information for two main detection problems: 1) two variants of a spatial search problem and 2) a fault detection problem for a three tank system (TTS). In all cases the assumption is that data may be collected from multiple sensors. The goal is then to combine all the information to determine whether an object (or fault) is present in a given area. In our setting there exist two main types of sensors, namely: 'small' and 'large' sensors. Essentially, it is assumed that small sensors can inspect an area that is relatively small in comparison to that which the large sensor can inspect. By deriving a relationship between small and large sensor measurements we combine data using a maximum likelihood based methodology. In particular, each detection problem is initially formulated as a system identification problem. Here, the large sensor collects data on the full system while small sensors collect data on subsystems. By establishing a connection of this identification problem to existing literature, we can obtain asymptotic convergence and asymptotic normality results. © 2015 American Automatic Control Council.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{She2015366,
author={She, Y. and Zhang, X. and Wang, Q. and Wu, Q.},
title={The potential relationship discovery model based on result fusion for biomedical medicine research},
journal={Journal of Information Science},
year={2015},
volume={41},
number={3},
pages={366-382},
doi={10.1177/0165551515578395},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930393036&doi=10.1177%2f0165551515578395&partnerID=40&md5=bb2e79f8487fffe06666c5f146ea59e6},
affiliation={Software School, Xiamen University, China},
abstract={With the amount of biomedical data growing explosively, medical scientists use many datasets in research for new medicine. However, the amount of biomedical data is growing too fast to abstract hidden information. At the same time, with the development of data storage diversification, scientists prefer to have data fusion based on heterogeneous data sources as opposed to a single data source, and ultimately to achieve knowledge and discovery across heterogeneous databases. Our study focuses on extending the application of latent semantic analysis methodologies into the area of biomedical research. Our purpose is to develop a model for discovering potential relationships between medicines and diseases based on biomedical latent semantic analysis. This model could be used in constructing link maps for biomedical entities, and provide a theoretical basis and practical support for biomedical scientists in their study of the disease-medicine relationship. In detail, we discuss the study of the integration of the latent semantic analysis model and data fusion methodologies. Our result fusion solution combines scientific literature repositories and a biomedical database based on context and the ABC model, and is supervised by a semi-supervised learning algorithm and data fusion algorithms. The expectation is that fused data could represent multilevel potential relationships between biological entities and related emotional relationship expression. The model is validated by experience and proven to be feasible and effective. © The Author(s) 2015.},
author_keywords={Emotion analysis;  heterogeneous data fusion;  latent semantic analysis},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Marhoubi2015,
author={Marhoubi, A.H. and Saravi, S. and Edirisinghe, E.A.},
title={The application of machine learning in multi sensor data fusion for activity recognition in mobile device space},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9481},
doi={10.1117/12.2177115},
art_number={94810G},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946223313&doi=10.1117%2f12.2177115&partnerID=40&md5=410791aa67d672324f1042acbeb24946},
affiliation={Computer Science Department, Loughborough University, United Kingdom},
abstract={The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion. © 2015 SPIE.},
author_keywords={Accelerometer;  Activity Recognition;  Gyroscope;  Mobile phone;  Mobile Sensors;  Multi Sensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou2015359,
author={Zhou, L.a  b  and Zhang, Y.a  and An, S.a  and Pan, Z.a  and Wang, K.a },
title={The development and implementation of a low-cost integrated control system for aerial survey},
journal={International Journal of Control and Automation},
year={2015},
volume={8},
number={4},
pages={359-372},
doi={10.14257/ijca.2015.8.4.33},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929300005&doi=10.14257%2fijca.2015.8.4.33&partnerID=40&md5=9b25c4fd2aea06ff9593925b3b478c55},
affiliation={School of Electronic Information, Wuhan University, China; Department of Electrical and Computer Engineering, University of Calgary, Canada},
abstract={This paper presents the development of a low-cost integrated control system for aerial survey based on pan-tilt and multi-sensor data fusion. The design of aerial vehicle route planning, pan-tilt mechanical structure and attitude estimation are discussed in detail. A route planning file containing flight paths and automatic exposure points is firstly derived by the route plan software to control the whole aerial photography task. As for the most challenging task, accurate attitude estimation, a custom Kalman filtering hardware module based on FPGA is introduced. The introduced module invokes the build-in multipliers of FPGA to achieve Kalman filtering hardware implementation, which leads to a rapid attitude estimation. After that mechanical structure of a specialized pan-tilt is designed and fabricated to adjust the camera attitude to the vertical direction of the ground plane. The practice aerial survey is also conducted to evaluate the accuracy and performance of designed control system. Our studies indicates that with the designed control system, it would be feasible to configure an aerial survey system while cooperate with professional aerial camera or some other civil cameras, which will make it easier to focusing on evaluation of aerial survey concepts and approaches. © 2015 SERSC.},
author_keywords={Aerial survey;  Aerial vehicle route planning;  Kalman filter;  Multi-sensor data fusion keyword;  Pan-tilt},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Hannachi2015,
author={Hannachi, A.a  and Kohler, S.b  and Lallement, A.a  and Hirsch, E.a },
title={A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9534},
doi={10.1117/12.2182832},
art_number={95340X},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931383422&doi=10.1117%2f12.2182832&partnerID=40&md5=7307cb5a68b2acd4d5b7c35034c98f4f},
affiliation={ICUBE, Laboratoire des Sciences de L'ingenieur de L'informatique et de L'imagerie, UMR 7357 CNRS-Universite de Strasbourg, CS 10413, Illkirch cedex, France; MIPS, Laboratoire Modelisation Intelligence Processus Systemes, EA 2332, UHA, 12 rue des freres Lumiere, Mulhouse Cedex, France},
abstract={3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions. © 2015 SPIE.},
author_keywords={3D reconstruction;  multi-sensor data fusion;  quality control;  Stereo-vision;  structured light},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ghesmat20151286,
author={Ghesmat, M.a  and Khalkhali, A.b },
title={Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm},
journal={COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering},
year={2015},
volume={34},
number={4},
pages={1286-1301},
doi={10.1108/COMPEL-10-2014-0274},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937020137&doi=10.1108%2fCOMPEL-10-2014-0274&partnerID=40&md5=87b5851b05d706ba4eab0846498cf184},
affiliation={Instrumentation and Automation Department, Petroleum University of Technology, Ahvaz, Iran; Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran},
abstract={Purpose: There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant. Design/methodology/approach: A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A Multi-Sensor Data Fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults. Findings: The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated. Originality/value: A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults. © Emerald Group Publishing Limited.},
author_keywords={Fault analysis;  Fault tolerance;  Fault tolerant control system;  Filters;  Multi-Sensor Data Fusion;  Non-linear control systems;  Nonlinear model predictive control;  Optimal control;  Unscented Kalman Filter},
document_type={Article},
source={Scopus},
}

@ARTICLE{Valdes201554,
author={Valdes, A. and Skinner, K.},
title={Probabilistic alert correlation},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2015},
volume={2212},
pages={54-68},
note={cited By 92},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947603083&partnerID=40&md5=f5eedc8678679bc718bbdab9b36ee42a},
affiliation={SRI International, United States},
abstract={With the growing deployment of host and network intrusion detection systems, managing reports from these systems becomes critically important. We present a probabilistic approach to alert correlation, extending ideas from multisensor data fusion. Features used for alert correlation are based on alert content that anticipates evolving IETF standards. The probabilistic approach provides a unified mathematical framework for correlating alerts that match closely but not perfectly, where the minimum degree of match required to fuse alerts is controlled by a single configurable parameter. Only features in common are considered in the fusion algorithm. For each feature we define an appropriate similarity function. The overall similarity is weighted by a specifiable expectation of similarity. In addition, a minimum similarity may be specified for some or all features. Features in this set must match at least as well as the minimum similarity specification in order to combine alerts, regardless of the goodness of match on the feature set as a whole. Our approach correlates attacks over time, correlates reports from heterogeneous sensors, and correlates multiple attack steps. © Springer-Verlag Berlin Heidelberg 2001.},
author_keywords={Adaptive systems;  Alert management;  Network security;  Sensor correlation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lorencs201581,
author={Lorencs, A. and Mednieks, I. and Sinica-Sinavskis, J.},
title={Classification of multisensor images with different spatial resolution},
journal={Elektronika ir Elektrotechnika},
year={2015},
volume={21},
number={5},
pages={81-85},
doi={10.5755/j01.eee.21.5.13333},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944073845&doi=10.5755%2fj01.eee.21.5.13333&partnerID=40&md5=57fbd11b32675c9d6c2d6f247e19916b},
affiliation={Institute of Electronics and Computer Science, Dzerbenes St. 14, Riga, Latvia},
abstract={The paper is focused on the analysis of classification possibilities of multisensor data with different spatial resolutions using combined classifiers based on Bayes approach with equal prior probabilities and on minimum of the Mahalanobis distance. The task set up for the 2014 IEEE GRSS Data Fusion Contest was chosen as an application example. High resolution RGB image and lower resolution thermal infrared image from the same urban area were processed to perform classification of each higher resolution pixel. Development of a fast and straightforward procedure was targeted and combined classifiers are proposed for that, exploiting spectral features from each data set separately. It is shown that data fusion can be achieved using the proposed classifiers and improvement of classification quality can be obtained with respect to the cases where only one of the data sets is used. The best classification results were obtained using the combined Bayes- Type classifier that provided overall classification accuracy of about 95 % when the ground truth pixels from the high resolution RGB image were used both for design and testing.},
author_keywords={Data fusion;  Hyperspectral imaging;  Image classification;  Remote sensing},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ma2015,
author={Ma, M. and An, J.},
title={Combination of evidence with different weighting factors: A novel probabilistic-based dissimilarity measure approach},
journal={Journal of Sensors},
year={2015},
volume={2015},
doi={10.1155/2015/509385},
art_number={509385},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926647619&doi=10.1155%2f2015%2f509385&partnerID=40&md5=b2b37dc8fd3f9378d9e9fae450c07a5c},
affiliation={Key Laboratory of Embedded and Network Computing of Hunan Province, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China},
abstract={To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster's rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness. © 2015 Mengmeng Ma and Jiyao An.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zhang2015486,
author={Zhang, W.a  and Zuo, J.a  and Guo, Q.a  and Ling, Z.b },
title={Multisensor information fusion scheme for particle filter},
journal={Electronics Letters},
year={2015},
volume={51},
number={6},
pages={486-488},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924727670&partnerID=40&md5=600fa2701defd306103c2beb3e9450f6},
affiliation={School of Aeronautics, Northwestern Polytechnical University, Xi'an, China; School of Electrical and Information Engineering, Hunan University, Changsha, China},
abstract={A two-stage information fusion scheme is proposed for particle filter. Its main characteristic is that the multisensor data is fused not only in the likelihood model but also in the stage of importance sampling. By incorporating the multisensor data in sequence, the proposal density can be refined step by step; thus high-quality particles and, consequently, a more accurate estimate can be obtained. © The Institution of Engineering and Technology 2015.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2015,
author={Wang, F. and Hu, L. and Zhou, J. and Zhao, K.},
title={A Data Processing Middleware Based on SOA for the Internet of Things},
journal={Journal of Sensors},
year={2015},
volume={2015},
doi={10.1155/2015/827045},
art_number={827045},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939817913&doi=10.1155%2f2015%2f827045&partnerID=40&md5=16089d009c2a431b0c08dd2ad4946fce},
affiliation={College of Computer Science and Technology, Jilin University, Changchun, China},
abstract={The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system. © 2015 Feng Wang et al.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Cappello2015714,
author={Cappello, F. and Ramasamy, S. and Sabatini, R. and Liu, J.},
title={Low-cost sensors based Multi-Sensor Data Fusion techniques for RPAS Navigation and Guidance},
journal={2015 International Conference on Unmanned Aircraft Systems, ICUAS 2015},
year={2015},
pages={714-722},
doi={10.1109/ICUAS.2015.7152354},
art_number={7152354},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941027448&doi=10.1109%2fICUAS.2015.7152354&partnerID=40&md5=828fa6fde5122c2ccf022670f8172ff5},
affiliation={School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC, Australia},
abstract={In order for Remotely Piloted Aircraft Systems (RPAS) to coexist seamlessly with manned aircraft in non-segregated airspace, enhanced navigational capabilities are essential to meet the Required Navigational Performance (RNP) levels in all flight phases. A Multi-Sensor Data Fusion (MSDF) framework is adopted to improve the navigation capabilities of an integrated Navigation and Guidance System (NGS) designed for small-sized RPAS. The MSDF architecture includes low-cost and low weight/volume navigation sensors suitable for various classes of RPAS. The selected sensors include Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). A loosely integrated navigation architecture is presented where an Unscented Kalman Filter (UKF) is used to combine the navigation sensor measurements. The presented UKF based VBS-INS-GNSS-ADM (U-VIGA) architecture is an evolution of previous research performed on Extended Kalman Filter (EKF) based VBS-INS-GNSS (E-VIGA) systems. An Aircraft Dynamics Model (ADM) is adopted as a virtual sensor and acts as a knowledge-based module providing additional position and attitude information, which is pre-processed by an additional/local UKF. The E-VIGA and U-VIGA performances are evaluated in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) by exploring a representative cross-section of this RPAS operational flight envelope. The position and attitude accuracy comparison shows that the E-VIGA and U-VIGA systems fulfill the relevant RNP criteria, including precision approach in CAT-II. A novel Human Machine Interface (HMI) architecture is also presented, whose design takes into consideration the coordination tasks of multiple human operators. In addition, the interface scheme incorporates the human operator as an integral part of the control loop providing a higher level of situational awareness. © 2015 IEEE.},
author_keywords={Aircraft dynamics model;  Human machine interface;  Low-cost sensors;  Multi-sensor data fusion;  Navigation and guidance system;  Remotely piloted aircraft systems;  Unscented kalman filter},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Schmitt2015,
author={Schmitt, R. and Fürtjes, T. and Kimmelmann, W. and Kosse, P. and Buratti, A.},
title={Quality monitoring in mass production of multiaxial non-crimp fabrics by using a multisensor system},
journal={XXI IMEKO World Congress "Measurement in Research and Industry"},
year={2015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951105815&partnerID=40&md5=225df596172499049db268815d3865ec},
affiliation={Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Department of Production Metrology, Aachen, Germany},
abstract={Due to their excellent mechanical properties and light weight, fiber-reinforced plastic (FRP) has a huge potential as a way to reduce vehicle and airplane weight and is therefore a subject of interest for aerospace and automotive industries. To mass-produce FRP products, certain textile structures are commonly used as semifinished products, such as multiaxial non-crimp fabrics (NCF). Currently, there is no automated monitoring system capable of ensuring quality in NCF production. The purpose of this study is to develop tools and concepts that could lead to fully-automated quality control of non-crimp fabrics. Useful sensor concepts were developed based on a thorough requirement analysis. Sensor data fusion strategies were conceived and evaluated to combine image processing information and basis weight sensor information with the purpose of detecting relevant quality features in a textile sample. The developed algorithm was tested on a textile sample and a corresponding defect map was generated. Results showed that it is possible to identify common textile defect types by combining these two sensors.},
author_keywords={Fiber-reinforced plastics;  Image processing;  Quality control;  Radiography;  Sensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhang2015151,
author={Zhang, Z. and Chen, H. and Xu, Y. and Zhong, J. and Lv, N. and Chen, S.},
title={Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding},
journal={Mechanical Systems and Signal Processing},
year={2015},
volume={60},
pages={151-165},
doi={10.1016/j.ymssp.2014.12.021},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925941483&doi=10.1016%2fj.ymssp.2014.12.021&partnerID=40&md5=6bed9e28c7c12c51c089722d8af3c89c},
affiliation={Institute of Welding Engineering, Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, China},
abstract={Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding. © 2015 Elsevier Ltd. All rights reserved.},
author_keywords={Arc welding;  Defect detection;  Feature extraction;  Feature selection;  Multisensory data fusion;  SVM-CV},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2015,
author={Wang, H.a  and Huang, Z.a  b  and Zhong, N.a  c  and Huang, J.a  and Han, Y.d  and Zhang, F.d },
title={An intelligent monitoring system for the safety of building structure under the W2T framework},
journal={International Journal of Distributed Sensor Networks},
year={2015},
volume={2015},
doi={10.1155/2015/378694},
art_number={378694},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940771343&doi=10.1155%2f2015%2f378694&partnerID=40&md5=b0465a573894d8d6a8a1c16616ccc494},
affiliation={International WIC Institute, Beijing University of Technology, Beijing, China; Knowledge Representation and Reasoning Group, Vrije University Amsterdam, Amsterdam, Netherlands; Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan; China Academy of Building Research, Beijing, China},
abstract={Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers' workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service. © 2015 Haiyuan Wang et al.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Figueira2015305,
author={Figueira, N.M.a  c  and Freire, I.L.b  and Trindade, O.c  and Simões, E.c },
title={Mission-oriented sensor arrays and UAVS-A case study on environmental monitoring},
journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives},
year={2015},
volume={40},
number={1W4},
pages={305-312},
doi={10.5194/isprsarchives-XL-1-W4-305-2015},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974575173&doi=10.5194%2fisprsarchives-XL-1-W4-305-2015&partnerID=40&md5=e6a973932fcd664317ffbe95f2ee3a2b},
affiliation={Brazilian Army, Dept. of Science and Technology, Quartel-General Do Exército, 30 Andar, Brasília, Brazil; Dept. of Computer Science, Minas Gerais Federal University, Av. Antônio Carlos, 6627, Belo Horizonte, Brazil; São Paulo University, São Paulo, Av. Trabalhador São Carlense, São Carlos, Brazil},
abstract={This paper presents a new concept of UAV mission design in geomatics, applied to the generation of thematic maps for a multitude of civilian and military applications. We discuss the architecture of Mission-Oriented Sensors Arrays (MOSA), proposed in Figueira et Al. (2013), aimed at splitting and decoupling the mission-oriented part of the system (non safety-critical hardware and software) from the aircraft control systems (safety-critical). As a case study, we present an environmental monitoring application for the automatic generation of thematic maps to track gunshot activity in conservation areas. The MOSA modeled for this application integrates information from a thermal camera and an on-the-ground microphone array. The use of microphone arrays technology is of particular interest in this paper. These arrays allow estimation of the direction-of-arrival (DOA) of the incoming sound waves. Information about events of interest is obtained by the fusion of the data provided by the microphone array, captured by the UAV, fused with information from the termal image processing. Preliminary results show the feasibility of the on-the-ground sound processing array and the simulation of the main processing module, to be embedded into an UAV in a future work. The main contributions of this paper are the proposed MOSA system, including concepts, models and architecture.},
author_keywords={Environmental monitoring;  Model driven development;  MOSA;  Multi-sensor data fusion;  Sound source localization;  Thematic maps},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ilisei2015,
author={Ilisei, A.-M. and Bruzzone, L.},
title={Multiresolution fusion of radar sounder and altimeter data for the generation of high resolution DEMs of ice sheets},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9643},
doi={10.1117/12.2196395},
art_number={96430X},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961590900&doi=10.1117%2f12.2196395&partnerID=40&md5=309406ef65ef83b29fbcd2eb6ff42b07},
affiliation={University of Trento, Dept. Information Engineering and Computer Science, Via Sommarive 5, Trento, Italy},
abstract={Understanding the dynamics and processes of the ice sheets is crucial for predicting the behavior of climate change. A potential approach to achieve this is by using high resolution (HR) digital elevation models (DEMs) of the ice surface derived from remote sensing radar or laser altimeters. Unfortunately, at present HR DEMs of large portions of the ice sheets are not available. To address this issue, in this paper we propose a multisensor data fusion technique for the generation of a HR DEM of the ice sheets, which fuses two types of data, i.e., radargrams acquired by radar sounder (RS) instruments and ice surface elevation data measured by altimeter (ALT) instruments. The aim of the technique is to generate a DEM of the ice surface at the best possible horizontal resolution by exploiting the complementary characteristics of the RS and ALT data. This is done by defining a novel processing scheme that involves image processing techniques based on data rescaling, geostatistical interpolation and multiresolution analysis (MRA). The method has been applied to a subset of RS and ALT data acquired over a portion of the Byrd Glacier in Antarctica. Experimental results confirm the effectiveness of the proposed method. © 2015 SPIE.},
author_keywords={altimeter;  data fusion;  DEM;  ice sheet;  radar sounder;  remote sensing},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Hua2015,
author={Hua, Z.-X. and Chen, X.-D.},
title={Multisensor Track Occupancy Detection Model Based on Chaotic Neural Networks},
journal={International Journal of Distributed Sensor Networks},
year={2015},
volume={2015},
doi={10.1155/2015/896340},
art_number={896340},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937796207&doi=10.1155%2f2015%2f896340&partnerID=40&md5=e6dc575fa2b9009d9b4c6dc218301302},
affiliation={School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China},
abstract={Bad shunting of track circuit is one of the major risks for railway traffic safety. The occupancy of track will not be correctly detected due to bad shunting, which could severely degrade the efficiency of the train dispatching command, sometimes even causing serious accidents, such as train collision and derailment. To handle the bad shunting problem, the Three Points Test Method is commonly used for detecting track occupancy. However, this method completely relies on manual confirmation and it thus usually leads to low detection efficiency and high labor intensity. In order to improve the detection efficiency and involve as less human labors as possible, this paper proposes a multisensor track occupancy detection model which is based on chaotic neural networks. This model uses the detection results of track occupancy collected by multiple sensors as the fundamental data, and then it calculates their weights using chaotic neural networks for data fusion, and finally the model determines whether the track is occupied. Experimental results and field tests demonstrate that the proposed model is able to provide track occupancy detection with high effectiveness and efficiency. Moreover, the accuracy of detection reaches 99.9999%, which can help to greatly reduce the labor intensity of manual confirmation. © 2015 Ze-xi Hua and Xiang-dong Chen.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li20151,
author={Li, Y.a  b  and Zhao, M.a  and Hao, G.b  and Li, J.a  and Jin, H.a },
title={Multisensor distributed fusion wiener deconvolution estimator for linear stochastic multichannel ARMA signal},
journal={International Journal of Control and Automation},
year={2015},
volume={8},
number={5},
pages={1-12},
doi={10.14257/ijca.2015.8.5.01},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930580303&doi=10.14257%2fijca.2015.8.5.01&partnerID=40&md5=aed6136972686619f3b01d9570f4bab3},
affiliation={School of Computer and Information Engineering, Harbin University of Commerce, HeiLongJiang, Harbin, China; Electronic Engineering Institute, Heilongjiang University, Heilongjiang, Harbin, China},
abstract={Multisensor distributed fusion Wiener deconvolution estimator is presented in this paper. It does not need to solve the Diophantine equation, and the steady-state Kalman filter gain of the augmented system. It can handle processing of nonstationary signal. White noise estimator and Astrom predictor are used in the algorithm. Gevers-Wouters (G-W) algorithm is also used in this paper. In order to improve the estimation precision, multisensor information fusion Wiener deconvolution estimator for multichannel system is proposed in this paper by using the modern time series analysis method. The information fusion algorithms included matrix weighted, diagonal matrices weighted, scalar weighted and covariance intersection fusion in this paper. Under the linear minimum variance optimal information fusion criterion, the calculation formula of optimal weighting coefficients have be given. The algorithm analyzes the relationship between the accuracy and the computation complexities of four fusion algorithms. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. It can be applied in signal processing, communication, control field and other fields. A simulation example for multichannel ARMA signal shows its correctness and effectiveness. © 2015 SERSC.},
author_keywords={Distributed information fusion;  Multichannel ARMA signal;  Wiener deconvolution estimator},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jiang2015493,
author={Jiang, W. and Li, Y. and Rizos, C.},
title={An optimal data fusion algorithm based on the triple integration of PPP-GNSS, INS and terrestrial ranging system},
journal={Lecture Notes in Electrical Engineering},
year={2015},
volume={342},
pages={493-505},
doi={10.1007/978-3-662-46632-2_43},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943276340&doi=10.1007%2f978-3-662-46632-2_43&partnerID=40&md5=96d27291ab992988a2ee0d6297cb0749},
affiliation={Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia},
abstract={This paper describes the integration of Locata, GNSS and INS technologies within a loosely-coupled triple integration algorithm. The conventional methods for multi-sensor integration can be classified as either centralised filtering or decentralised filtering. Centralised Kalman filtering (CKF) provides globally optimal state estimation by directly combining measurement data. However CKF system has some disadvantages such as a comparatively large computational burden and poor fault detection and isolation ability. Decentralised Kalman filtering (DKF) addresses such defects while aiming to achieve the same accuracy as a centralised filter. On the other hand global optimal filtering (GOF) can achieve a higher accuracy than the traditional CKF because it utilises more information resources than the CKF. In the information space, the information resources that can be used for estimation include the measurements, the local predictions, and the global predictions. In order to evaluate the system performance, a field experiment was conducted on a vehicle with different kinds of maneuvers, including circular motion and accelerated motion. The results indicate that: (1) GOF-based PPP-GNSS/ Locata/INS integration system can provide better positioning accuracy compared with CFK and federated Kalman filtering; (2) covariance analysis shows that the GOF improves the system estimation covariance; and (3) a comparison of GOF with local filters confirms the superiority of a GOF-based triple integration system. © Springer-Verlag Berlin Heidelberg 2015.},
author_keywords={Data fusion;  GNSS;  INS;  Locata;  Multisensor system},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cho2015416,
author={Cho, S.a  and Park, J.-W.b  and Bae, M.a  and Sim, S.-H.a },
title={Bridge deflection estimation using kalman filter for data fusion},
journal={Life-Cycle of Structural Systems: Design, Assessment, Maintenance and Management - Proceedings of the 4th International Symposium on Life-Cycle Civil Engineering, IALCCE 2014},
year={2015},
pages={416-419},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941248601&partnerID=40&md5=156d10ec893b82f7c667aeb5c5834edc},
affiliation={Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea; University of Illinois, Urbana-Champaign, IL, United States},
abstract={Measuring bridge deflection under dynamic loading is useful to understand structural behavior as well as to assess health condition. However, the conventional approaches for the deflection measurements in large-scale bridges are challenging as they typically require fixed reference points that may be often unavailable in the fields. Alternatively, indirect estimation methods have been developed; other measurements such as acceleration or strain are first obtained and subsequently transformed to displacement. As the acceleration and strain are conveniently measured, the indirect estimation methods are considered to be effective and practical while they have in general lower accuracy in estimation compared to the conventional direct measurement. This study proposes a multisensor fusion approach combining acceleration and strain for bridge deflection estimation with improved accuracy. A mathematical formulation based on Kalman filtering is presented for the fusion of the different physical quantities to estimation vertical deflections. Both numerical and experimental validations are presented to show the efficacy and accuracy of the proposed approach. © 2015 Taylor & Francis Group, London.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Johnson2015,
author={Johnson, K.a  and Minor, C.b },
title={Non-specific sensor arrays for chemical detection},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9455},
doi={10.1117/12.2177392},
art_number={94550G},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948760408&doi=10.1117%2f12.2177392&partnerID=40&md5=afd0b1d76557741df7b656b4827f5a05},
affiliation={Naval Research Laboratory, Washington, DC, United States; Nova Research Inc., Alexandria, VA, United States},
abstract={Non-specific chemical sensor arrays have been the subject of considerable research efforts over the past thirty years with the idea that, by analogy to vertebrate olfaction, they are potentially capable of rendering complex chemical assessments with relatively modest logistical footprints. However, the actual implementation of such devices in challenging "real world" scenarios has arguably continued to fall short of these expectations. This work examines the inherent limitations of such devices for complex chemical sensing scenarios, placing them on a continuum between simple univariate sensors and complex multivariate analytical instrumentation and analyzing their utility in general-purpose chemical detection and accurate chemical sensing in the presence of unknown "unknowns." Results with simulated and acquired data sets are presented with discussion of the implications in development of chemical sensor arrays suitable for complex scenarios. © 2015 SPIE.},
author_keywords={Data fusion;  Interferants;  Multisensor systems;  Open-world sensing},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Nie201561,
author={Nie, Q. and Xia, J. and Qian, Z. and An, C. and Cui, Q.},
title={Use of multisensor data in reliable short-term travel time forecasting for urban roads: Dempster-Shafer approach},
journal={Transportation Research Record},
year={2015},
volume={2526},
pages={61-69},
doi={10.3141/2526-07},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975856216&doi=10.3141%2f2526-07&partnerID=40&md5=b1c0b616290622902989dcdf3844a2d5},
affiliation={Intelligent Transportation System Research Center, Southeast University, 35 Jinxianghe Road, Xuanwu District, Nanjing, China},
abstract={As multiple traffic data sources have become available recently, a new opportunity has been provided for improving the accuracy of shortterm travel time forecasting by fusing different but valid data sources. However, previous studies seldom quantified and integrated the reliability of data sources into model development to achieve the potential promised by data fusion. This paper proposes a combined method for short-term travel time forecasting for urban road links that uses travel time extracted from fixed vehicle detectors and probe vehicle data. The method uses the generalized autoregressive conditional heteroscedasticity model to forecast the mean and variance of each type of travel time data source, and the Dempster-Shafer model is used to calculate the fusion weights iteratively. Real-world data collected on urban roads in Kunshan, China, were used to validate and evaluate the proposed method. Empirical results show that the proposed method can effectively capture the variance of each type of travel time data source for iteratively calculating the fusion weights and hence can produce accurate travel time forecasts. Moreover, through a comparison with the alternative methods, the proposed method is shown to be able to consistently generate improved performance under varying traffic conditions.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Sarim2015,
author={Sarim, M.a  and Nemati, A.b  and Kumar, M.a  and Cohen, K.c },
title={Extended kalman filter based quadrotor state estimation based on asynchronous multisensor data},
journal={ASME 2015 Dynamic Systems and Control Conference, DSCC 2015},
year={2015},
volume={1},
doi={10.1115/DSCC2015-9929},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973301087&doi=10.1115%2fDSCC2015-9929&partnerID=40&md5=e38e1af2472f4c760c43c1aa31894b36},
affiliation={Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, United States; Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States; Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, United States},
abstract={For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario. Copyright © 2015 by ASME.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Gaska2015915,
author={Gaska, T.a  and Carpenter, A.b  and Chen, Y.b },
title={Future avionic system hybrid processor pooled architectures},
journal={Annual Forum Proceedings - AHS International},
year={2015},
volume={2},
number={January},
pages={915-928},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937720393&partnerID=40&md5=2dff5102030d6e402296a944d5e06ac9},
affiliation={Lockheed Martin MST, Owego, NY, United States; Binghamton University, Binghamton, NY, United States},
abstract={Next generation avionics Size Weight and Power (SWaP) can benefit from transformational improvements and flexibility in processing brought on by Moore's Law with proper heterogeneous pooled processor solutions. It is no longer feasible to simply use a modest number of network-connected single-core processors in isolated subsystems; instead, multicore processing is the norm By 2016-2018, there will be on-chip multicore processors with 16 or more cores on each die integrated with on-chip transformational multi-Teraflop General Purpose Graphics Processing Units (GPGPUs). Heterogeneous reprogrammable System-on-Chip (SoC) devices will include multicore processors mixed with 5 billion transistor Field Programmable Gate Arrays (FPGAs). At this time, many avionics subsystems are just beginning the integration of multicore into safe and secure systems. Similarly, sensor critical avionics systems are still adjusting their heterogeneous processing mix in multicore, FPGA, and GPGPU processing solutions. Pooled processing across subsystems is still very limited in deployment. Subsystem level partitioning and security/safety considerations often still limit the potential SWAP improvement for pooled processing if not properly planned and managed. Legacy software migration also continues to make pooled processing in tech refresh a challenge. This paper presents results of an initial investigation into relevant dual use parallels from adjacent markets with similar challenges. One of the adjacent markets that can be used for open discussion of the heterogeneous pooled processor challenge is the driverless car processor architecture. There are already developments with Teraflop computing in the glove compartment, modular multi-sensor data fusion sensor software, integrated information and infrastructure hardware security, and standards for on-platform/off-platform cloud integration. Future cars will have hardware that runs up to 100 Million lines of code. What can the avionics industry leverage from this adjacent market as it moves forward in hybrid pooled processor architectures?. © 2015 by the American Helicopter Society International, Inc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Demirkol2015165,
author={Demirkol, A.a  and Demir, Z.b  and Emre, E.c },
title={A unifying approach to track-to-track correlation for multisensor fusion for multiple targets},
journal={Optoelectronics and Advanced Materials, Rapid Communications},
year={2015},
volume={9},
number={1-2},
pages={165-177},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946166446&partnerID=40&md5=9233d95e3ebaf835afebdf5e77b10cf1},
affiliation={Sakarya University, Department of Electrical and Electronics Engineering, Sakarya, Turkey; Anadolu University, Vocational School of Porsuk, Eskisehir, Turkey; Sakarya University, Department of Computer Engineering, Sakarya, Turkey},
abstract={In this paper, a global modeling approach was proposed for multi sensor fusion problems. Once the global model was investigated considering the data association and fusion, it is adapted to track to track correlation problem by a new approach. The key development of the approach is that a decentralized filtering algorithm is used for data fusion and state estimation problems in a multi-target tracking system. The use of a global mapping matrix for the track to track correlation is key element of our technique. Via the presented mathematical models, the sensor fusion and track to track correlation problems can be solved in a global way. © 2015, National Institute of Optoelectronics. All rights reserved.},
author_keywords={Data association;  Decentralized Kalman filtering;  Multi-sensor fusion;  Multi-target tracking;  Track to track correlation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jawahar2015203,
author={Jawahar, A. and Rao, S.K. and Karishma, Sk.B.},
title={Target estimation analysis using data association and fusion},
journal={International Journal of Oceans and Oceanography},
year={2015},
volume={9},
number={2},
pages={203-210},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952917162&partnerID=40&md5=1d25de489ec582bc18ead9d8e253dcf6},
affiliation={School of Electrical Sciences, KLEF, KL University, Vaddeswaram, India},
abstract={In underwater scenario, the well-known problem of track-to-track association exists in multisensor, multitarget tracking system, where each sensor has its own set of target tracks. A predominant question is how to decide whether two tracks from different sensors correspond to the same target or different. If, it is decided that two such tracks represent the same target, then next issue is how to combine the corresponding state estimates. The first process is termed as Data Association (DA) and next process is termed as Data Fusion (DF) in tar-get motion analysis (TMA). In this paper, the algorithm normalized-squared innovation suggested by Y. Bar-Shalom has been explored for towed array and hull mounted array measurements. Furthermore, particle filter (PF) is used for TMA. © 2015 Research India Publications.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li201515,
author={Li, Y.a  b  and Zhao, M.a  and Hao, G.b  and Li, J.a  and Jin, H.a },
title={Multisensor distributed information fusion white noise wiener deconvolution estimator},
journal={International Journal of Control and Automation},
year={2015},
volume={8},
number={4},
pages={15-24},
doi={10.14257/ijca.2015.8.4.3},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929314928&doi=10.14257%2fijca.2015.8.4.3&partnerID=40&md5=4e22447803c2e1064da259f239ac7280},
affiliation={School of Computer and Information Engineering, Harbin University of Commerce, HeiLongJiang, Harbin, China; Electronic Engineering Institute, Heilongjiang University, Heilongjiang, Harbin, China},
abstract={Multisensor distributed information fusion white noise wiener deconvolution estimator is presented in this paper. The algorithm is using the modern time series analysis method and white noise estimator under the linear minimum variance optimal fusion criterion. Gevers-Wouters (G-W) algorithm are also used in this paper. This paper presents information fusion algorithm including scalar weighted and covariance intersection fusion. The algorithm analyzes the relationship between the accuracy and the computation of the two fusion algorithm. The formula of optimal weighting coefficients is given. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. It can be applied to signal processing in oil seismic exploration, communication and other fields. A simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter shows its correctness and effectiveness. © 2015 SERSC.},
author_keywords={Distributed information fusion;  Reflection seismology;  White noise estimator;  Wiener deconvolution estimator},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li2015201,
author={Li, Y.a  b  and Zhao, M.a  and Hao, G.b  and Li, J.a  and Jin, H.a },
title={Multisensor information fusion state estimator for descriptor and non descriptor systems},
journal={International Journal of Control and Automation},
year={2015},
volume={8},
number={4},
pages={201-212},
doi={10.14257/ijca.2015.8.4.20},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929313506&doi=10.14257%2fijca.2015.8.4.20&partnerID=40&md5=02e78ee966db4c565950310b9cca268d},
affiliation={School of Computer and Information Engineering, Harbin University of Commerce, HeiLongJiang, Harbin, China; Electronic Engineering Institute, Heilongjiang University, Heilongjiang, Harbin, China},
abstract={Multisensor distributed information fusion state estimator is presented in this paper. The algorithm deals with descriptor and non descriptor discrete time time-invariant stochastic linear system which is described by state space model. Under the assumption of the observability of the system, the state of the system is a linear combination of input white noise, observation white noise and observation signal. Further non-recursive state estimators algorithm is presented, which can be computed by the white noise estimators and measurement predictor. In order to improve the accuracy of the state estimator, this paper presents information fusion algorithm including matrix weighted, diagonal matrices weighted, scalar weighted and covariance intersection fusion, in the sense of linear minimum variance. The formula of optimal weighting coefficients is given. The algorithm analyzes the relationship between the accuracy and the computation of the four fusion algorithm. A simulation example for non descriptor system with 3 sensors shows its correctness and effectiveness. © 2015 SERSC.},
author_keywords={Descriptor and non descriptor systems;  Distributed information fusion;  State estimator},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lyu201542,
author={Lyu, Y.a  and Yan, F.b  and Chen, Y.a  and Wang, D.b  and Shi, Y.a  and Agoulmine, N.c },
title={High-performance scheduling model for multisensor gateway of cloud sensor system-based smart-living},
journal={Information Fusion},
year={2015},
volume={21},
number={1},
pages={42-56},
doi={10.1016/j.inffus.2013.04.004},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904819180&doi=10.1016%2fj.inffus.2013.04.004&partnerID=40&md5=8ffaa8cae62bd340409b2f2e38f21f5b},
affiliation={OS Lab., Research Institute of Information Technology, Tsinghua University, No. 1 Tsinghua Yuan, Haidian District, Beijing 10084, China; Department of Computer Science and Technology, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100022, China; IBISC Laboratory, University of Evry, 1 Rue Ambroise Croizat, 91000 Évry, Paris, France},
abstract={In cloud sensor system-based smart-living applications, large-scale distributed sensors may be deployed to collect information and report to the manipulator and the cloud. A gateway is often employed as infrastructure in this scenario, acting as the data collector, the relay and the agency of the initial multisensor fusion, and thus must be able to handle as many concurrent requests as possible from diverse sensors of different vendors. This study proposes a high-performance scheduling model with a cloud-supported caching mechanism for the gateway of the cloud sensor system-based smart-living. Scheduling and caching optimization are performed by 0-1 programming combined with the periodic task models. Correlation analyses of the simulated results determine the most effective factors to the performance, and the performance tests with the selected factors show that a gateway with 2.4 G-uniCPU/4 G-memory/300 G-harddisk can support the system with one million sensors registered in the cloud and 5000 concurrent live sensors through it, with a 25× gain throughput improvement compared to the traditional application-type scheduling. © 2013 Elsevier B.V. All rights reserved.},
author_keywords={Cloud sensor system;  Cyber physical system;  Internet of things;  Multisensor fusion;  Periodic application},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Pohl2015,
author={Pohl, C.a  and Loong, C.K.a  and Van Genderen, J.b },
title={Multisensor approach to oil palm plantation monitoring using data fusion and GIS},
journal={ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings},
year={2015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964048175&partnerID=40&md5=932e9c71334cc2fc286507e9763efe49},
affiliation={Geoscience and Digital Earth Centre (INSTeG), Universiti Teknologi Malaysia (UTM), Block T06/FGHT, Johor Bahru, Johor, Malaysia; Department of Earth Observation Science, University of Twente, P.O. Box 6, Enschede, Netherlands},
abstract={Oil palm is recognized as the golden crop. It produces the highest oil yield among oil seed crops. Malaysia, as the world's second largest producer of palm oil, has 16 % of its lands planted with oil palms. To cope with the ever-increasing global demand on edible oil, additional areas of oil palm are forecasted to increase globally by 12 to 19 million hectares by 2050. Multisensor remote sensing plays an important role by providing relevant, timely and accurate information that can be developed into a plantation monitoring system to optimize production and sustainability. The use of synthetic aperture radar (SAR), a form of microwave remote sensing, in combination with visible and infrared (VIR) data has several distinct advantages, the biggest benefits being daylight and weather independent. SAR produces 'cloud-free' images. However, SAR

repository: nidiascampos/smartgreen

repo_name: nidiascampos/smartgreen

branch_name: refs/heads/master

gha_language: Python