text:
cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84956911794&doi=10.1155%2f2016%2f1903792&partnerID=40&md5=2039fd8082624bd6776cc829018e239f},
affiliation={College of Information and Communication Engineering, Harbin Engineering University, Harbin, China; College of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin, China},
abstract={ATR system has a broad application prospect in the military field, especially in the field of modern defense technology. When paradoxes are in existence in ATR system due to adverse battlefield environment, integration cannot be effectively and reliably carried out only by traditional DS evidence theory. In this paper, a modified DS evidence theory is presented and applied in IR/MMW target recognition system. The improvement of DS evidence theory is realized by three parts: the introduction of sensor priority and evidence credibility to realize the discount processing of evidences, the modification of DS combination rule to enhance the accuracy of synthesis results, and the compound decision-making rule. The application of the modified algorithm in IR/MMW system is designed to deal with paradoxes, improve the target recognition rate, and ensure the reliability of target recognition system. Experiments are given to illustrate that the introduction of the modified DS evidence theory in IR/MMW system is better able to realize satisfactory target recognition performance through multisensor information fusion than any single-mode system. © 2016 Yibing Li et al.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Aliakbarpour20168264,
author={Aliakbarpour, H.a  and Prasath, V.B.S.a  and Palaniappan, K.a  and Seetharaman, G.b  and Dias, J.c  d },
title={Heterogeneous multi-view information fusion: Review of 3-D reconstruction methods and a new registration with uncertainty modeling},
journal={IEEE Access},
year={2016},
volume={4},
pages={8264-8285},
doi={10.1109/ACCESS.2016.2629987},
art_number={7755753},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012979726&doi=10.1109%2fACCESS.2016.2629987&partnerID=40&md5=7e5f91a6fbbf194fe89c5a6e938bdeef},
affiliation={Computational Imaging and Vis Analysis Laboratory, Department of Computer Science, University of Missouri-Columbia, Columbia, MO, United States; Advanced Computing Concepts, U.S. Naval Research Laboratory, Washington, DC, United States; Faculty of Science and Technology, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Robotics Institute, Khalifa University of Science, Technology and Research, Abu Dhabi, United Arab Emirates},
abstract={We consider a multisensor network fusion framework for 3-D data registration using inertial planes, the underlying geometric relations, and transformation model uncertainties. We present a comprehensive review of 3-D reconstruction methods and registration techniques in terms of the underlying geometric relations and associated uncertainties in the registered images. The 3-D data registration and the scene reconstruction task using a set of multiview images are an essential goal of structure-from-motion algorithms that still remains challenging for many applications, such as surveillance, human motion and behavior modeling, virtual-reality, smart-rooms, health-care, teleconferencing, games, human-robot interaction, medical imaging, and scene understanding. We propose a framework to incorporate measurement uncertainties in the registered imagery, which is a critical issue to ensure the robustness of these applications but is often not addressed. In our test bed environment, a network of sensors is used where each physical node consists of a coupled camera and associated inertial sensor (IS)/inertial measurement unit. Each camera-IS node can be considered as a hybrid sensor or fusion-based virtual camera. The 3-D scene information is registered onto a set of virtual planes defined by the IS. The virtual registrations are based on using the homography calculated from 3-D orientation data provided by the IS. The uncertainty associated with each 3-D point projected onto the virtual planes is modeled using statistical geometry methods. Experimental results demonstrate the feasibility and effectiveness of the proposed approach for multiview reconstruction with sensor fusion. © 2013 IEEE.},
author_keywords={3D reconstruction;  coupled sensors;  geometric uncertainty;  heterogeneous information fusion;  homography;  image registration;  inertial measurement unit (IMU);  sensor network;  Structure-from-motion;  virtual reality},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Maria2015,
author={Maria, A.R.a  b  and Sever, P.a  and Carlos, V.b },
title={Biomedical sensors data fusion algorithm for enhancing the efficiency of fault-tolerant systems in case of wearable electronics device},
journal={Proceedings - 2015 8th Romania Tier 2 Federation Grid, Cloud and High Performance Computing in Science, ROLCG 2015},
year={2015},
doi={10.1109/ROLCG.2015.7367228},
art_number={7367228},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964018935&doi=10.1109%2fROLCG.2015.7367228&partnerID=40&md5=f6be95cd62b13d3c9795ae5f57ae80a2},
affiliation={Faculty of Electronic, Telecommunications and Information Technology, Politehnica University of Bucharest, Bucharest, Romania; Mons University, Faculty of Engineering, Department of Electronics and Microelectronics, Mons, Belgium},
abstract={The paper presents multisensor data fusion process and algorithm modeling. The multisensor system consists in a set of heterogeneous medical sensors for biomedical electronics. Multisensor data fusion is based on artificial intelligence, pattern recognition and statistical estimation. The goal of the modeling is to reduce uncertainty of the data values tracked from sensors. The big data collected from sensors can contain also noisy data and repetitive one which can lead to inefficiencies in data storage. In this case is appropriate to reduce the errors by using algorithms and data fusion. © 2015 Nat'l Inst. for Research and Development for Isotopic and Molecular Technology.},
author_keywords={algoritm;  data fusion;  medical;  multisensor;  sensors},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Gyurjyan20151938,
author={Gyurjyan, V.a  and Bartle, A.b  and Lukashin, C.c  and Mancilla, S.d  and Oyarzun, R.d  and Vakhnin, A.e },
title={Component based dataflow processing framework},
journal={Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015},
year={2015},
pages={1938-1942},
doi={10.1109/BigData.2015.7363971},
art_number={7363971},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963717954&doi=10.1109%2fBigData.2015.7363971&partnerID=40&md5=d6def0b6fa12299483d99b3a24645d35},
affiliation={TJNAF, Newport News, VA, United States; Mechdyne, Co., Virginia Beach, VA, United States; NASA Langley Research Center, Hampton, VA, United States; Universidad Técnica Federico Santa María, Chile; Science Systems and Applications Inc., Hampton, VA, United States},
abstract={In this paper we present SOA based CLAs12 event Reconstruction and Analyses (CLARA) framework used to develop Earth Science multi-sensor data fusion, processing, and analytics applications (NAIADS: NASA JLAB collaboration). CLARA design focus is on two main traits: a) real-time data stream processing, and b) service oriented architecture (SOA) in a flow based programming (FBP) paradigm. Data driven and data centric architecture of CLARA presents an environment for developing agile, elastic, multilingual data processing applications. The CLARA framework presents solutions, capable of processing large volumes of data interactively and substantially faster than batch systems. © 2015 IEEE.},
author_keywords={data fusion;  data-driven;  data-flow;  earth science;  publish-subscribe;  service oriented},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang2015320,
author={Wang, S.a  and Zhang, X.a  b },
title={An approach for fall detection of older population based on multi-sensor data fusion},
journal={2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015},
year={2015},
pages={320-323},
doi={10.1109/URAI.2015.7358963},
art_number={7358963},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962696351&doi=10.1109%2fURAI.2015.7358963&partnerID=40&md5=6cdf742c55f08b7ab68cb0745f287bf1},
affiliation={Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'An Jiaotong University, Xi'an, China; School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, China},
abstract={For the elderly may have the risk of falling during using the walking assistant robot, an approach for fall detection of older population based on multi-sensor data fusion is presented in this paper, which is making full use of tactile-slip sensor, acceleration sensor and gyroscope to acquire the older falling data and extract its feature. And then, the multi-sensor data fusion based on the BP neural network is realized to get the value of falling risk. Finally, the verifying experimental result shows that the proposed method can effectively distinguish the fall events and other daily life activities, and gets good result in predicting falls. © 2015 IEEE.},
author_keywords={Data fusion;  Fall prediction;  Multi-sensor},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sun2015327,
author={Sun, X. and Yan, G. and Zhang, B.},
title={Weighted measurement fusion fractional order Kalman estimator},
journal={ICCAIS 2015 - 4th International Conference on Control, Automation and Information Sciences},
year={2015},
pages={327-331},
doi={10.1109/ICCAIS.2015.7338686},
art_number={7338686},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960342257&doi=10.1109%2fICCAIS.2015.7338686&partnerID=40&md5=6d8dde2d6e01b8f07d17aab4df0797e6},
affiliation={Department of Automation, Heilongjiang University, Harbin, China},
abstract={A weighted measurement fusion fractional order Kalman estimator is presented for the multisensor linear discrete fractional state-space systems with different measurement matrices and uncorrelated noises. It is rigorously proved that the weighted measurement fusion Kalman estimator is numerically identical to the centralized fusion Kalman estimator, so that it has the global optimality. The simulation example for a 3-sensors fractional order system shows its effectiveness. © 2015 IEEE.},
author_keywords={discrete fractional state-space systems;  fractional Kalman filter;  information fusion;  weighted measurement fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ghosal2015,
author={Ghosal, M. and Rao, V.},
title={Fusion of PMU and SCADA Data for dynamic state estimation of power system},
journal={2015 North American Power Symposium, NAPS 2015},
year={2015},
doi={10.1109/NAPS.2015.7335239},
art_number={7335239},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961832198&doi=10.1109%2fNAPS.2015.7335239&partnerID=40&md5=de98c75441d3fbb2a872c799d080edfb},
affiliation={Department of Electrical and Computer Engineering, Texas Tech Univefsity, Lubbock, TX, United States},
abstract={In recent times dynamic state estimation has been identified as a requirement for the modern power system. The availability of Phasor Measurement Units (PMUs) has made the tracking of power system dynamic states in real time quite feasible. However, the measurement system of today's grid consists of a combination of 1) traditional slow SCADA data with low sampling rates and 2) fast PMUs having high sampling rates. Such measurements with different sampling rates pose a serious challenge in integrating them into power system dynamic state estimator. This paper addresses this problem as a special case of multi-rate multi-sensor data fusion problem and appropriately adopts a method to integrate PMU and SCADA data in the power system dynamic state estimator. The proposed method has been demonstrated using a IEEE 14 bus test case. © 2015 IEEE.},
author_keywords={Dynamic state estimation;  Kalman filtering;  multi sensor data fusion;  phasor measurement units;  SCADA;  smart grid},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Si201528772,
author={Si, L.a  b  and Wang, Z.a  and Liu, X.a  and Tan, C.a  and Xu, J.a  and Zheng, K.a },
title={Multi-sensor data fusion identification for shearer cutting conditions based on parallel quasi-newton neural networks and the dempster-shafer theory},
journal={Sensors (Switzerland)},
year={2015},
volume={15},
number={11},
pages={28772-28795},
doi={10.3390/s151128772},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947252560&doi=10.3390%2fs151128772&partnerID=40&md5=5c4fe807cc393f4890012c51fc684834},
affiliation={School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China},
abstract={In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed methodperforms with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully me hanized coal mining face was demonstrated to specify the effect of the proposed system. © 2015 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Cutting condition identification;  Dempster-Shafer theory;  Feature extraction;  Neural network;  Parallel quasi-newton algorithm;  Shearer},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Savastano20151909,
author={Savastano, S. and Guida, R.},
title={Multi-sensor data fusion for long range demining area reduction},
journal={International Geoscience and Remote Sensing Symposium (IGARSS)},
year={2015},
volume={2015-November},
pages={1909-1912},
doi={10.1109/IGARSS.2015.7326167},
art_number={7326167},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962614982&doi=10.1109%2fIGARSS.2015.7326167&partnerID=40&md5=6f72157105317e861ad208330e057f58},
affiliation={Surrey Space Centre, University of Surrey, Guildford, United Kingdom},
abstract={Multisensor data fusion is getting more importance with the increasing number of available satellite sensors. The aim of data fusion is to take advantages of combining different types of data to improve accuracies. However features extracted from different sensors will often have different statistical properties, and therefore combining data in an efficient way is not a trivial task. This paper proposes a new algorithm for data fusion between classification maps separately derived by application of clustering algorithms to PolSAR and Multi-spectral datasets. The expected new output is a map where all the classes identified with each single dataset will be present. The pixels assigned to the same class with both datasets will be characterized by a higher likelihood to belong to that class. The application for which the data fusion has been developed is that of hazardous areas reduction in land mines clearing operation. At this purpose a demonstration site has been set up in Poland within the FP7 D-BOX project and the fusion framework tested on it with acquisition of remote sensing imagery. © 2015 IEEE.},
author_keywords={Clustering;  Data fusion;  Multispectral;  PolSAR;  Remote Sensing},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Anitha2015209,
author={Anitha, M.a  and Kurahatti, N.G.b },
title={Adaptive beamforming using neural network and fuzzy logic model for measurement data fusion},
journal={2015 International Conference on Communication and Signal Processing, ICCSP 2015},
year={2015},
pages={209-214},
doi={10.1109/ICCSP.2015.7322870},
art_number={7322870},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966649651&doi=10.1109%2fICCSP.2015.7322870&partnerID=40&md5=dacb414bee72b66e9825673dd8e213c8},
affiliation={Department of Telecommunication Engineering, Sir MVIT, Bangalore, India; Electronics and Communication Department, EPCET, Bangalore, India},
abstract={In the information technology age, multisource multi-sensor information fusion encompasses the theory, methods and tools conceived and used for exploiting synergy in the information acquired from multiple sources databases, sensors. In Multi-sensor data fusion technology, array processing is an area of signal processing that has powerful tools for extracting information from signals collected using an array of sensors. A sensor array captures spatially propagating signals arriving from a certain direction and processes them to obtain useful information. To this end, we intend to linearly combine the signals from all the sensors with coefficients in a manner, so as to estimate transmitted data radiating from a particular direction. Current methods to calculate the weight coefficients are complex. simple and effective weight coefficient calculation method is considered and which is based on where the best features of neural network and fuzzy logic is combined. This technique is based on the optimum beamformer and makes it robust to an faulty estimate of the Direction Of Arrival (DOA) even when powerful interferences are within the uncertainty range of the desired source or contact. The new modified beamformer algorithm is the product of an effort to provide more efficient procedure for real time implementation and a better estimate of the position and spectrum of the contact which is helpful in measurement data fusion or localization. © 2015 IEEE.},
author_keywords={Beamformer;  neural network and Fuzzy logic;  Sensor network},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Mirzaei20156625,
author={Mirzaei, G.a  and Jamali, M.M.b  and Ross, J.c  and Gorsevski, P.V.c  and Bingman, V.P.c },
title={Data fusion of acoustics, infrared, and marine radar for avian study},
journal={IEEE Sensors Journal},
year={2015},
volume={15},
number={11},
pages={6625-6632},
doi={10.1109/JSEN.2015.2464232},
art_number={7177050},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962485219&doi=10.1109%2fJSEN.2015.2464232&partnerID=40&md5=f1f3348bb5f0c4bb2394a5f643b716f2},
affiliation={Department of Computer Science and Engineering, Ohio State University at Marion, Marion, OH, United States; University of Toledo, Toledo, OH, United States; Geospatial Science School of Earth and Environment, Bowling Green State University, Bowling Green, OH, United States},
abstract={A multisensor data fusion approach via acoustics, infrared camera, and marine radar is proposed and described in the application of avian monitoring. The ultimate scope of avian monitoring is to preserve the population of birds and bats, especially those listed in the endangered list, by observing their activity and behavior over the migration period. With the significant attention toward the construction of off-shore/on-shore wind farms in recent decades, the wind turbines are more threatening the avian life with increasing the risk of birds/bats collision with turbine blades. In order to address this problem, this paper proposes a fuzzy Bayesian-based multisensory data fusion approach to provide the activity information regarding the targets in the application of avian monitoring. The developed technique is used to process the Spring and Fall 2011 migration period. © 2001-2012 IEEE.},
author_keywords={acoustics;  data fusion;  infrared;  radar;  Sensor},
document_type={Article},
source={Scopus},
}

@ARTICLE{Völker20151,
author={Völker, C.a  and Shokouhi, P.b },
title={Clustering Based Multi Sensor Data Fusion for Honeycomb Detection in Concrete},
journal={Journal of Nondestructive Evaluation},
year={2015},
volume={34},
number={4},
pages={1-10},
doi={10.1007/s10921-015-0307-7},
art_number={32},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947419113&doi=10.1007%2fs10921-015-0307-7&partnerID=40&md5=7b82c79cc845bbb4f0b22f24356b137a},
affiliation={BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, Berlin, Germany; Department of Civil and Environmental Engineering, The Pennsylvania State University, 215 Sackett Bldg., University Park, PA, United States},
abstract={We use three clustering algorithms to aggregate a three-modal non-destructive testing data set into defect and not-defect groups. Our data set consist of impact-echo, ultrasound (US) and ground penetrating radar data collected on a large concrete slab with embedded simulated honeycombing defects. US performs best in defect discriminating and sizing, however the false positive rate is still high. We fuse the data set using K-Means, Fuzzy C-Means and DBSCAN clustering at feature-level. We discern that DBSCAN improves the detectability up to 10 %. A discussion of its advantages over commonly used K-Means and Fuzzy C-Means clustering are provided. © 2015, Springer Science+Business Media New York.},
author_keywords={Clustering;  Concrete evaluation;  Data fusion;  Honeycombing;  Machine learning},
document_type={Article},
source={Scopus},
}

@ARTICLE{Nazemzadeh20152965,
author={Nazemzadeh, P.a  and Moro, F.a  and Fontanelli, D.b  and Macii, D.b  and Palopoli, L.a },
title={Indoor Positioning of a Robotic Walking Assistant for Large Public Environments},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2015},
volume={64},
number={11},
pages={2965-2976},
doi={10.1109/TIM.2015.2437637},
art_number={7152904},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959350328&doi=10.1109%2fTIM.2015.2437637&partnerID=40&md5=9e80b2ccc36bad21425cf01f8bd4d38f},
affiliation={Department of Information Engineering and Computer Science, University of Trento, Trento, Italy; Department of Industrial Engineering, University of Trento, Trento, Italy},
abstract={Indoor localization and position tracking are essential to support applications and services for ambient-assisted living. While the problem of indoor localization is still open and already quite complex per se, in large public places, additional issues of cost, accuracy, and scalability arise. In this paper, the position estimation and tracking technique developed within the project devices for assisted living (DALi) is described, analyzed through simulations, and finally validated by means of a variety of experiments on the field. The goal of the DALi project is to design a robotic wheeled walker guiding people with psychomotor problems. Indeed, people with motor or cognitive impairments are often afraid of moving in large and crowded environments (e.g., because they could lose the sense of direction). In order to mitigate this problem, the position tracking approach described in this paper is based on multisensor data fusion and it is conceived to assure a good tradeoff between target accuracy, level of confidence, and deployment costs. Quite interestingly, the same approach could be used for indoor automated guided vehicles and robotics. © 1963-2012 IEEE.},
author_keywords={Assistive devices;  Kalman filtering;  navigation;  performance evaluation;  position measurement;  sensor fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li2015440,
author={Li, M.a  and Liu, S.a  and Ma, Y.a  and Sun, C.b  and Jia, Y.c },
title={Descent image based landing area terrain reconstruction technology for lunar landing mission},
journal={Imaging Science Journal},
year={2015},
volume={63},
number={8},
pages={440-446},
doi={10.1179/1743131X15Y.0000000029},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951282935&doi=10.1179%2f1743131X15Y.0000000029&partnerID=40&md5=93f997b5dd29424f6d072a0c0c6c3b3e},
affiliation={Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; Computational Informatics, CSIRO, North Ryde, NSW, Australia; Beijing Institute of Spacecraft System Engineering, Beijing, China},
abstract={In this paper, we present a descent image based extraterrestrial terrain reconstruction technology, which has been successfully applied in China’s first soft landing Chang’E-3 mission on moon. When scouting on the moon surface, the lunar rover (Jade Rabbit rover) requires reliable terrain information for navigation and positioning tasks. The descent image sequence acts as a link between the orbital images and the surface images. The proposed approach collects multilevel descent images, as well as inertial information from an inertial measurement unit and surface stereo vision information, to generalise a consistent surface model. A data fusion strategy is proposed to track the camera trajectory, and refined plane sweep stereo matching and 3D alignment technologies are analysed in this article. The onboard result, as well as on-earth simulation experiments, has shown that the discussed techniques are reliable to reconstruct the terrain information for the lunar rover’s localisation and navigation. © 2015 The Royal Photographic Society.},
author_keywords={3D alignment;  Data fusion;  Descent images;  Plane sweep},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Feng-De2015196,
author={Feng-De, Q.a  and Feng-Wu, W.a  and Jiang, L.a  and Guan-Jun, T.b },
title={A Method for the Measurement of Ship Attitude Based on Multi-sensor Data Fusion},
journal={Proceedings - 2015 9th International Conference on Frontier of Computer Science and Technology, FCST 2015},
year={2015},
pages={196-199},
doi={10.1109/FCST.2015.14},
art_number={7314674},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961746353&doi=10.1109%2fFCST.2015.14&partnerID=40&md5=52c0a070be23feb450e7600988b3e3c9},
affiliation={Navigation College, Dalian Maritime University, Dalian, China; Tianjin AIDS to Navigation, Navigation Guarantee Centre of North China Sea, Tianjin, China},
abstract={This paper describes a new method for the measurement of ship attitude which is based on multi-sensor data fusion, and advances a data fusion mathematical algorithm. By means of calculating the data that acquires from the ship attitude sensors which are installed on the training ship motor vessel 'YUKUN' of Dalian maritime university, the results show this method is relatively reliable. And under the circumstances of limited sensors, this method calculates easily and can get a more accurate measurement result compared with arithmetic mean value. © 2015 IEEE.},
author_keywords={Data Fusion;  Multi-sensor;  Ship Attitude},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Khalid2015,
author={Khalid, H.M. and Peng, J.C.-H.},
title={Immunity toward data-injection attacks using multisensor track fusion-based model prediction},
journal={IEEE Transactions on Smart Grid},
year={2015},
volume={PP},
number={99},
doi={10.1109/TSG.2015.2487280},
art_number={7302596},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945425015&doi=10.1109%2fTSG.2015.2487280&partnerID=40&md5=f86a4915873296c6b0cff774a4746523},
affiliation={Department of Electrical Engineering and Computer Science, Institute Center for Energy, Masdar Institute, Abu Dhabi, United Arab Emirates},
abstract={Utilization of synchrophasor measurements for wide-area monitoring applications enables system operators to acquire real-time grid information. However, intentional injections of false synchrophasor measurements can potentially lead to inappropriate control actions, jeopardizing the security, and reliability of power transmission networks. To resolve this issue, a multisensor track-level fusion-based model prediction (TFMP) has been proposed. It has been demonstrated on a mature wide-area monitoring application, which detect electromechanical oscillations. In this paper, to extract the initial correlation information about attacked oscillation parameters, Kalmanlike particle filter (KLPF)-based smoother has been used at each monitoring node. To reduce its computational burden, the KLPF-based smoother is diagonalized into subsystems. The scheme is further supported by the characteristics of moving horizon estimates for handling continuous load fluctuations and perturbations caused by data injections in power grids. Performance evaluations are conducted using different datainjection scenarios in the IEEE New England 39 Bus system. Results show the proposed TFMP accurately extracted oscillatory parameters from the contaminated measurements in the presence of multiple system disturbances and random data injections. Â© 2015 IEEE.},
author_keywords={Interarea oscillation;  Kalman filter;  Model prediction;  Multisensor data fusion;  Phasor measurement unit (PMU);  Power system stability;  Synchrophasor;  Track-level measurement fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yang2015459,
author={Yang, C.a  b  and Yang, Z.a  and Deng, Z.a },
title={Guaranteed cost robust weighted measurement fusion steady-state Kalman predictors with uncertain noise variances},
journal={Aerospace Science and Technology},
year={2015},
volume={46},
pages={459-470},
doi={10.1016/j.ast.2015.08.014},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941558571&doi=10.1016%2fj.ast.2015.08.014&partnerID=40&md5=6a024039c89bccfdb76c4a4948e0d811},
affiliation={Department of Automation, Heilongjiang University, Harbin, China; Heilongjiang College of Business and Technology, Harbin, China},
abstract={Two classes of guaranteed cost robust weighted measurement fusion (WMF) one-step and multi-step Kalman predictors are presented by the Lyapunov equation approach for multisensor system with uncertain noise variances based on the minimax robust estimation principle. One class is to construct a maximal perturbation region of uncertain noise variances such that for all admissible perturbations in this region, the deviations of its actual accuracies with respect to the robust accuracy are guaranteed to remain within the prescribed range, and the maximal lower bound and minimal upper bound of accuracy deviations are given. The other class is to find minimal upper bound and maximal lower bound of accuracy deviations under given perturbation region of uncertain noise variances. The general and unified concept of guaranteed cost robustness is presented. The proof of the guaranteed cost robustness is presented by the Lyapunov equation approach. A simulation example shows the correctness and effectiveness of the proposed results. © 2015 Elsevier Masson SAS.},
author_keywords={Guaranteed cost robustness;  Lyapunov equation approach;  Minimax robust Kalman predictor;  Multisensor system;  Uncertain noise variance;  Weighted measurement fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang20152769,
author={Wang, X. and Hu, H. and Liu, X.},
title={Multisensor Data Fusion Techniques With ELM for Pulverized-Fuel Flow Concentration Measurement in Cofired Power Plant},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2015},
volume={64},
number={10},
pages={2769-2780},
doi={10.1109/TIM.2015.2421713},
art_number={7097697},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959527918&doi=10.1109%2fTIM.2015.2421713&partnerID=40&md5=cbd32204af773e2a70211ead9d6ef3a5},
affiliation={State Key Laboratory of Power Equipment and Electrical Insulation, Xi'an Jiaotong University, Xi'an, China},
abstract={Due to the phase concentration, measurement of coal/biomass/air three-phase flow is closely associated with the quantity of pollutant discharged and combustion efficiency in the pneumatic conveying system. This paper proposed a multisensor data fusion technique for the online volume concentration measurement of coal/biomass pulverized-fuel flow in cofired power plant. The techniques combine electrostatic sensors with capacitance sensors and incorporates a data fusion technique based on an adaptive wavelet neural network (AWNN), and gradient descent learning algorithm and genetic learning algorithm are used for training of the network parameters. The flow regime is first identified by performing an extreme learning machine on the electrostatic fluctuation signals, thus making the concentration measurement less affected by variations in the flow regimes. Then under the certain identified flow regime, the AWNN data fusion method was applied to determine the phase concentration. An experimental platform was built for phase concentration measurement of pulverized coal (PC)/biomass/air three-phase flow, and the test results confirmed that the technology of the concentration measurement with flow regime identification is better than that of the one without, and the maximum fiducial errors of sawdust and PC are 2.1% and 1.2%. © 1963-2012 IEEE.},
author_keywords={Adaptive wavelet neural network (AWNN);  concentration measurement;  extreme learning machine (ELM);  multisensor data fusion;  three-phase flow},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jiang20155634,
author={Jiang, W. and Li, Y. and Rizos, C.},
title={Optimal Data Fusion Algorithm for Navigation Using Triple Integration of PPP-GNSS, INS, and Terrestrial Ranging System},
journal={IEEE Sensors Journal},
year={2015},
volume={15},
number={10},
pages={5634-5644},
doi={10.1109/JSEN.2015.2447015},
art_number={7128322},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939227319&doi=10.1109%2fJSEN.2015.2447015&partnerID=40&md5=021ac0ba32ea5aa65baeb2aa3c09a944},
affiliation={School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia},
abstract={A good port management system must be able to perform safe, predictable, and efficient execution of transport processes. In order to improve the quality of the port management, a robust navigation system is required, which enables to provide the positions of vessels 24/7 on either open or impeded environment. This paper describes a robust georeferencing system that could satisfy centimeter-level accuracy requirements in port environments. The design is based on the loosely coupled integration of global navigation satellite system (GNSS) technology, a terrestrial radio frequency ranging system known as Locata, and an inertial navigation system (INS). GNSS observations are processed using the precise point positioning (PPP) approach instead of the conventional differential approach. To satisfy both accuracy and reliability requirements, three integration algorithms-centralized Kalman filtering (CKF), federated Kalman filtering (FKF), and global optimal filtering (GOF)-are investigated and implemented into a triple-integrated PPP-GNSS/Locata/INS system. A preliminary performance assessment, which is based on the analysis of real data, concludes that all the three integration algorithms are able to provide centimeter-level positioning solutions. The results show that the FKF and CKF algorithms have similar performance, whereas the GOF solution has higher accuracy. Moreover, the outlier simulation is conducted and the result verifies the outlier fault-tolerant capability of the GOF-based PPP-GNSS/Locata/INS integrated system. © 2014 IEEE.},
author_keywords={Global optimal filter;  INS;  Locata;  Multisensor Data fusion;  PPP-GNSS},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2015653,
author={Wang, S. and Song, S.-M. and Yu, H.},
title={Attitude fusion approach for multisensor with unknown noise characteristics},
journal={Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology},
year={2015},
volume={23},
number={5},
pages={653-661},
doi={10.13695/j.cnki.12-1222/o3.2015.05.017},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949194080&doi=10.13695%2fj.cnki.12-1222%2fo3.2015.05.017&partnerID=40&md5=4c0f801c65df4c9dac76be78b9f79272},
affiliation={Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, China},
abstract={A covariance intersection fusion estimation algorithm is presented for multi-sensor system with unknown covariance and noise characteristics, which means that the relativity and variance of the state noise and measurement noise are not available. Firstly, a corresponding CKF estimator is chosen for each subsystem to produce a local estimation according to the measurement data newly acquired. Secondly, based on the least matrix-weighted linear variance rule, a fast successive covariance intersection (SCI) fusion algorithm is proposed to achieve the optimal fusion estimation, which simplifies the multidimensional optimization problem into the optimization of several one-dimensional nonlinear cost functions. Thirdly, in the subsystems, a method of switching between error quaternion and error modified rodrigues parameter is used to avoid the norm constraint of quaternion and the singular problem of modified rodrigues parameter. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed algorithm. © 2015, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.},
author_keywords={Covariance intersection;  Cubature Kalman filter estimate;  Information fusion;  Quaternion;  Rodrigues parameter},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Abate20151450,
author={Abate, A.F.a  and Cantoni, V.b  and Nappi, M.c  and Narducci, F.a  and Ricciardi, S.a },
title={Hybrid multi-sensor tracking system for field-deployable mixed reality environment},
journal={Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015},
year={2015},
pages={1450-1455},
doi={10.1109/INDIN.2015.7281946},
art_number={7281946},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949526382&doi=10.1109%2fINDIN.2015.7281946&partnerID=40&md5=9e7ccaa8589032615dbae801df95c0c4},
affiliation={VRLab - DISTRA, University of Salerno, Salerno, Italy; Department of Industrial and Computer Engineering, University of Pavia, Pavia, Italy; BipLab - DISTRA, University of Salerno, Salerno, Italy},
abstract={Mixed reality has been proposed for industrial applications such as AR-assisted manteinance and repair in which real equipment are augmented by visual AIDS. A possible complementary use of this technology for the same context can be achieved by performing virtual training in a 'mixed reality environment' enabling to observe actual-size virtual replicas of real equipment within the physical space, providing an advantage in terms of perceptual impact and learning efficacy compared to traditional computer-based-training techniques. To this aim we present an effective hybrid tracking approach, exploiting both optical markers and inertial sensors to measure user's head position and rotation, providing both accuracy and robustness to fast head movements. A field-deployable tridimensionally-arranged marker-set, specifically designed for this kind of application, represents a further contribute of this paper, providing reliable close-to-medium distance optical tracking capabilities and increased robustness to challenging lighting conditions. First experiments confirm the potential of the proposed solution for this context and for many other applications as well. © 2015 IEEE.},
author_keywords={field-deployable marker set;  marker-based tracking;  mixed reality;  multisensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang2015100,
author={Wang, X.a  b  and Deng, Z.a },
title={Robust centralized fusion steady-state Kalman filter for multisensor uncertain systems},
journal={Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015},
year={2015},
pages={100-104},
doi={10.1109/ICEDIF.2015.7280170},
art_number={7280170},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963520673&doi=10.1109%2fICEDIF.2015.7280170&partnerID=40&md5=33ea6a3c7f1f0292278b40ba53f1fdb9},
affiliation={Department of Automation, Heilongjiang University, Harbin, China; Cheng-dong College of Northeast Agricultural University, Harbin, China},
abstract={For the linear discrete time multisensor system with uncertain model parameters and noise variances, the centralized fusion robust steady-state Kalman filter is presented by a new approach of compensating the parameter uncertainties by a fictitious noise. Based on the minimax robust estimation principle, a robust centralized fusion Kalman filter is presented based on the worst-case conservative systems with the conservative upper bounds of noise variances. It proves robustness by the Lyapunov approach. Its robust accuracy is higher than that of each local robust Kalman filter. A simulation example shows how to search the robust region of uncertain parameters and the good performance of the proposed robust Kalman filter. © 2015 IEEE.},
author_keywords={Fictitious noise;  Lyapunov equation approach;  Multisensor information fusion;  Robust Kalman filter;  Robust region;  Uncertain system},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ran2015114,
author={Ran, C. and Dou, Y. and Gao, Y.},
title={WMF information filter for multisensor descriptor system with same measurement matrix and correlated noises},
journal={Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015},
year={2015},
pages={114-118},
doi={10.1109/ICEDIF.2015.7280173},
art_number={7280173},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963553786&doi=10.1109%2fICEDIF.2015.7280173&partnerID=40&md5=7030123a20162de2d396acd4c0146c99},
affiliation={Department of Automation, Heilongjiang University, Harbin, China},
abstract={For the multisensor linear stochastic descriptor system with same measurement matrix and correlated noises, the weighted measurement fusion information filter is presented, based on the weighted measurement fusion algorithm and the Kalman information filtering theory. This information filtering is a new repression of Kalman filtering based on information matrix, which can reduce computational burden and has important application in many theory analysis. And the presented weighted measurement fusion information filter has global optimality, and can avoid computing these cross-variances of the local Kalman filters, compared with the state fusion method. A simulation example about 3-sensors stochastic descriptor system verifies the effectiveness. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang20158,
author={Wang, X. and Sun, S. and Shi, Y.},
title={Fault detection and noise variance identifier with cooperation fault-torlerance for multisensor system},
journal={Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015},
year={2015},
pages={8-12},
doi={10.1109/ICEDIF.2015.7280148},
art_number={7280148},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963543105&doi=10.1109%2fICEDIF.2015.7280148&partnerID=40&md5=319afcb6629330c9bc2967851865ef5a},
affiliation={Department of Automation, Heilongjiang University, Heilongjiang Harbin, China},
abstract={For the multisensor linear discrete time-invariant stochastic system with unknown noise variances, the new measurement system is constructed by using matrix pseudo-inverse method, which can yield many groups of new measurement sequences by cooperating work. Furthermore, the statistics characteristics of the new measurement sequences are analyzed to determine whether the sensors are faulting or not. The self-tuning cooperation fault-torlerance algorithm are presented. Meanwhile, the linear matrix equations are constructed about measurement noise variances. Then the measurement noise variances are found by solving the equations. A simulation example for a tracking system with 3-sensor shows its effectiveness. © 2015 IEEE.},
author_keywords={Cooperation fault-torlerance;  Linear matrix equations;  Self-tuning},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wang2015105,
author={Wang, J.a  and Gao, Y.a  and Ran, C.a  and Huo, Y.b },
title={State estimation with two-level fusion structure},
journal={Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015},
year={2015},
pages={105-109},
doi={10.1109/ICEDIF.2015.7280171},
art_number={7280171},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963570224&doi=10.1109%2fICEDIF.2015.7280171&partnerID=40&md5=f63c60f9d7e3b5eb6d56aefbe921ce8a},
affiliation={Automation Department, Heilongjiang University, Harbin, China; Harbin Environmental Monitoring Center Station, Harbin, China},
abstract={In order to obtain more accurate state estimation from multisensor system, the state estimation with two-level fusion structure is presented. By means of measurement fusion algorithm, the local first-level fusion centers can obtain the globally optimal fused measurement information, and then the local state estimation can be got by classical Kalman filtering. At the second-level fusion center, the fused estimation can be received by applying the covariance intersection fusion algorithm, which avoids the calculation of the correlation among local first-level fusion centers. The simulation example shows the effectiveness and higher accuracy of the presented fusion structure. © 2015 IEEE.},
author_keywords={Covariance intersection fusion;  Measurement fusion;  State estimation;  Two-level fusion structure},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yang201593,
author={Yang, C.a  b  and Deng, Z.a },
title={Robust measurement fusion steady-state Kalman predictor for multisensor uncertain system},
journal={Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015},
year={2015},
pages={93-99},
doi={10.1109/ICEDIF.2015.7280169},
art_number={7280169},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963594826&doi=10.1109%2fICEDIF.2015.7280169&partnerID=40&md5=6a2c36e5265ac438779b40706fea67d4},
affiliation={Department of Automation, Heilongjiang University, Harbin, China; Cheng-dong College of Northeast Agricultural University, Harbin, China},
abstract={For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement fusion Kalman predictor is presented based on the worst-case conservative system with the conservative upper bounds of noise variances. The robustness and robust accuracy relation prove by Lyapunov equation approach. It is prove that it is equivalent to the robust centralized fusion Kalman predictor, and its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness. © 2015 IEEE.},
author_keywords={Fictitious white noise;  Measurement fusion;  Minimax robust Kalman predictor;  Robust region;  Uncertain system},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Li2015830,
author={Li, T.a  and Corchado, J.M.a  and Bajo, J.b  and Sun, S.c },
title={Multi-source data clustering},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={830-837},
art_number={7266646},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960479150&partnerID=40&md5=ac9bf165de62eb1daf7ae733e3a82610},
affiliation={BISITE Group, Faculty of Science, University of Salamanca, Salamanca, Spain; Department of Artificial Intelligence, Technical University of Madrid, Madrid, Spain; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China},
abstract={In this paper, we consider a special multi-source data clustering problem for which the data-points from the same source cannot be grouped into the same cluster, namely cannot link (CL) constraint, and the sizes of the generated clusters are subject to maximum thresholds. No prior information is given about the level of clutter (namely noisy data) or the number of clusters. Particularly, the clusters might be closely distributed in the space (overlapping clusters) with one another and have to be carefully partitioned to meet the CL constraint. This particular CL constrained data mining problem corresponds to a significant problem of multi-sensor data fusion (MSDF) raised in the multi-target detection context. A novel clustering method as well as the online parameter learning procedure is proposed for this particular dataset model. Clustering results are provided to demonstrate the validity of the present approach. © 2015 IEEE.},
author_keywords={Clustering;  data fusion;  target detection},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Moshtagh20151641,
author={Moshtagh, N. and Chan, M.W.},
title={Multisensor fusion using homotopy particle filter},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={1641-1648},
art_number={7266753},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960539795&partnerID=40&md5=5a55fe769e1292e0e5ce86c699c84939},
affiliation={Advanced Technology Center, Lockheed Martin Space Systems Company, Sunnyvale, CA, United States},
abstract={Homotopy particle filters (HPFs), recently developed by Daum and Huang [1], present an alternative nonlinear filtering approach to sampling-based particle filters. Homotopy filters perform information update using the flow of particles to regions with high measurement likelihood. The particle flows in HPFs are solutions to a Fokker-Plank equation governing the dynamics of the posterior density function. The resulting partial differential equation is highly under-determined and has many solutions. In this work we study the nonzero-diffusion flow, and show its advantage in multisensor fusion. The nonzero-diffusion flow was chosen because it has the form of an information filter (inverse-covariance filter), and this feature makes it suitable for integration of measurement contributions from different sensors. The effectiveness of the HPF with nonzero diffusion flow as a fusion mechanism was evaluated for tracking a moving target using multiple range and bearing sensors. It is shown that the HPF requires several orders of magnitude fewer particles than a sampling-based particle filter, without loss in state estimator performance. © 2015 IEEE.},
author_keywords={Flow Control;  Homotopy Particle Filter;  Multisensor Fusion;  Target Tracking},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cao20151764,
author={Cao, W.a  and Lan, J.a  and Li, X.R.b },
title={Joint tracking and classification based on conditional joint decision and estimation},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={1764-1771},
art_number={7266769},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960533489&partnerID=40&md5=5d8a47cbc19d0308da96f961150858d0},
affiliation={Center for Information Engineering Science Research (CIESR), School of Electronics and Information Engineering, Xi'An Jiaotong University, Xi'an, Shaanxi, China; Department of Electrical Engineering, University of New Orleans, New Orleans, LA, United States},
abstract={In joint tracking and classification (JTC) problems, both decision and estimation are involved and they affect each other. Good solutions for JTC require solving the two problems jointly. A joint decision and estimation (JDE) framework based on a generalized Bayes risk was recently proposed for solving the problem of inter-dependent decision and estimation. In the JDE framework, a conditional JDE (CJDE) risk was proposed, and the corresponding optimal solution was obtained. Due to the development of modern sensor technology, multisensor data with different characteristics are available. In this paper, we solve a JTC problem using multisensor data by the CJDE method. First, a dynamic JTC problem based on kinematic and attribute measurements is formulated as a JDE problem. To solve this problem, we propose a multiple-model recursive CJDE (RCJDE) method, which is an extension of the original RCJDE to the multisensor scenario. For joint performance evaluation, we suggest two joint performance metrics (JPM) for the cases with known and unknown ground truth, respectively. Simulation results demonstrate the effectiveness of the proposed RCJDE method. They show that the multisensor data based RCJDE can outperform the traditional two-step strategies in JPM. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Meyer20151778,
author={Meyer, F.a  and Braca, P.b  and Willett, P.c  and Hlawatsch, F.a },
title={Scalable multitarget tracking using multiple sensors: A belief propagation approach},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={1778-1785},
art_number={7266771},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960540038&partnerID=40&md5=b720698ae880cffdfe5c21547c7a0272},
affiliation={Institute of Telecommunications, Vienna University of Technology, Vienna, Austria; NATO STO Centre for Maritime Research and Experimentation, La Spezia, Italy; Department of ECE, University of Connecticut, Storrs, CT, United States},
abstract={We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a 'detailed' factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. In particular, we observed that our method can outperform the sequential multisensor joint probabilistic data association filter (JPDAF) and performs similar to the Monte Carlo JPDAF. © 2015 IEEE.},
author_keywords={belief propagation;  data association;  factor graph;  message passing;  Multitarget tracking;  sensor network},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Razzaghi2015774,
author={Razzaghi, T.a  and Roderick, O.b  and Safro, I.a  and Marko, N.b },
title={Fast imbalanced classification of healthcare data with missing values},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={774-781},
art_number={7266639},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960539334&partnerID=40&md5=c51f1beca16dd5c67c12ece9a57943fe},
affiliation={School of Computing, Clemson Univeristy, Clemson, SC, United States; Geisinger Health System, Danville, PA, United States},
abstract={In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. The proposed method is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Conte2015476,
author={Conte, A. and Niu, R.},
title={Censoring in distributed radar tracking systems with various feedback models},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={476-483},
art_number={7266599},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960498412&partnerID=40&md5=64e45717573c90f6552d2b78229971be},
affiliation={Dept. of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States},
abstract={In this paper, various methods to control censoring of local state estimates in a distributed multisensor radar tracking system are proposed. The data flow architectures that are used include three different feedback methods in order to achieve the goal of adequate state estimation using the fewest number of local state estimates sent to the fusion center as possible. The main novelty introduced in this paper is the use of J-Divergence for censoring the local state estimates in conjunction with the various feedback architectures. A simulation was run on these architectures with three spatially diverse sensors and a simple non-maneuvering target. Results show that distributed architectures that provide feedback from the fusion center to the local censoring processes or to the local state estimators, yield better estimates with more one-way communication savings. © 2015 IEEE.},
author_keywords={Censoring;  Extended Kalman Filtering;  Information Fusion;  Tracking},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lu2015932,
author={Lu, J. and Niu, R.},
title={A state estimation and malicious attack game in multi-sensor dynamic systems},
journal={2015 18th International Conference on Information Fusion, Fusion 2015},
year={2015},
pages={932-936},
art_number={7266659},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960478477&partnerID=40&md5=b862416120b5b461e7943b90d98edafd},
affiliation={Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States},
abstract={In this paper, the problem of false information attack on and the Kalman filter's defense of state estimation in dynamic multi-sensor systems is investigated from a game theoretic perspective. The relationship between the Kalman filter and the adversary can be regarded as a two-person zero-sum game. Under which condition both sides of the game will reach the Nash equilibrium is investigated in the paper. The multisensor Kalman filter system and the adversary are supposed to be rational players. The Kalman filter and the adversary have to choose their respective subsets of sensors to perform system state estimation and false information injection. It is shown how both sides pick their strategies in order to gain more and lose less. The optimal solutions are achieved by solving the minimax problem. Numerical results are also provided in order to illustrate the effectiveness of the derived optimal strategies. © 2015 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lopez-Caloca2015,
author={Lopez-Caloca, A.A.},
title={Data fusion approach for Urban area identification using multisensor information},
journal={2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp 2015},
year={2015},
doi={10.1109/Multi-Temp.2015.7245783},
art_number={7245783},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959871575&doi=10.1109%2fMulti-Temp.2015.7245783&partnerID=40&md5=4625a9c43973ec5bd8a5e232fdb4a0bb},
affiliation={Centro de Investigación en Geografía y Geomática 'Ing. Jorge L. Tamayo, A. C. CentroGeo CONACYT México City, México, Mexico},
abstract={This article presents a procedure to identify and extract urban areas in medium resolution satellite images. At present, we have and continue to study various methodologies to process and extract information on urban surfaces, since urban growth is having environmental impacts on the involved ecological systems. The proposed method takes advantage of the fact that data fusion allows us to combine in an optimal manner, multiple sources of classifiers and to generate a single source of information. In this context, we propose the use of data fusion algorithms, by multiple classifiers, taking into account the spectral and spatial characteristics of the satellite data, which in our case are the Landsat ETM+ and the ENVISAT-ASAR. The developed system includes an ensemble fusion architecture and the use of algorithms such as Fuzzy K-mean and Markov Random Field (MRF). The study case is the Guadalajara metropolitan area, in Jalisco, Mexico, which has great growth and sprawl; in its surrounding areas there are regions which are interesting in terms of geothermal exploitation and with great ecological value. The experimental results, using the multiple classifier system (MCS) show the urban characteristics at the regional scale, offering results that are potentially significant at this scale and the direction of changes in urban growth. © 2015 IEEE.},
author_keywords={Data fusion;  multiple classifier system},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Long2015386,
author={Long, W. and Li, F. and Luo, L. and Zhang, X.},
title={The design of temperature and humidity Control System for Incubation based on data fusion and fuzzy decoupling},
journal={2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015},
year={2015},
pages={386-391},
doi={10.1109/ICMA.2015.7237516},
art_number={7237516},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955255420&doi=10.1109%2fICMA.2015.7237516&partnerID=40&md5=5fd0a45af21e1b1be1c406230daeb84f},
affiliation={School of Information Engineering, Nanchang University, Nanchang, Jiangxi Province, China},
abstract={In this paper, a control strategy based on data fusion and fuzzy decoupling is proposed to solve the problems of low reliability of sensor and temperature & humidity coupling in the temperature and humidity Control System for Incubation. Adaptive weighted fusion algorithm for multi-sensor data was used to fuse multiple temperature, humidity data collected indoor incubation. And at the same time, the fuzzy decoupling control rule is used to compensate the starting and stopping time of the temperature and humidity adjusting equipment. The results show that the system has the characteristics of control and integration, high reliability and good stability. © 2015 IEEE.},
author_keywords={Fuzzy decoupling;  Incubation room;  Multi-sensor data fusion;  Temperature and humidity},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Yuan20152427,
author={Yuan, J.a  b  and Chen, H.a  and Sun, F.a  and Huang, Y.a },
title={Multisensor Information Fusion for People Tracking with a Mobile Robot: A Particle Filtering Approach},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2015},
volume={64},
number={9},
pages={2427-2442},
doi={10.1109/TIM.2015.2407512},
art_number={7057671},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939230787&doi=10.1109%2fTIM.2015.2407512&partnerID=40&md5=cd25eadd8db51593194f0cc4b08a7b17},
affiliation={Department of Automation, Nankai University, Tianjin, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China},
abstract={People tracking based on multisensor information fusion is addressed. A framework is presented for fusing the laser range finder (LRF) data and the monocular camera data. Based on this framework, an LRF-based detection algorithm is proposed to identify the pairs of human legs, by combining motion information and metric features. Moreover, a geometric observation model is developed for the camera to extract both the range and bearing measurements of the target person by focusing the target's shoes with the camera. Then, a near-optimal particle filter is designed to fuse the measurements from the LRF and the camera. To prevent the sample impoverishment, a procedure of sample diversity improvement is used after the resampling step. The full occlusion problem is solved using image matching based on speeded up robust feature. Note that either of the LRF and the camera can work independently, since both the range and bearing are simultaneously acquired from the LRF or the camera. As a result, flexible and robust tracking can be achieved. Extensive experiments demonstrate that the proposed approach achieves high tracking accuracy and robustness. Especially, only a very small number of particles suffice to maintain good tracking performance. © 1963-2012 IEEE.},
author_keywords={Mobile robot;  multisensor fusion;  particle filter;  people tracking.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Liu201521857,
author={Liu, Z. and Guo, W. and Tang, Z. and Chen, Y.},
title={Multi-sensor data fusion using a relevance vector machine based on an ant colony for gearbox fault detection},
journal={Sensors (Switzerland)},
year={2015},
volume={15},
number={9},
pages={21857-21875},
doi={10.3390/s150921857},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940703968&doi=10.3390%2fs150921857&partnerID=40&md5=35ea02a93a9cfd538334441279b69e5a},
affiliation={School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, China},
abstract={Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV). © 2015 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Ant colony optimization;  Fault detection;  Gearboxes;  Multi-senor data fusion;  Relevance vector machine},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tao201555,
author={Tao, G.L.a  b  and Deng, Z.L.a },
title={Self-tuning fusion Kalman filter for multisensor single-channel ARMA signals with coloured noises},
journal={IMA Journal of Mathematical Control and Information},
year={2015},
volume={32},
number={1},
pages={55-74},
doi={10.1093/imamci/dnt027},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925591556&doi=10.1093%2fimamci%2fdnt027&partnerID=40&md5=fc8786940de970fdd5747aaab70137dd},
affiliation={Department of Automation, Heilongjiang University, Harbin, China; Computer and Information Engineering College, Heilongjiang University of Science and Technology, Harbin, China},
abstract={For the multisensor single-channel autoregressive moving average (ARMA) signals with a white measurement noise and autoregressive (AR) coloured measurement noises as common disturbance noises, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm, correlation method, and the Gevers-Wouters algorithm, and the fused estimators are obtained by taking the average of the local estimators. And they have the consistency. Substituting them into the optimal information fusion Kalman filter, a selftuning fusion Kalman filter with multi-model for single-channel ARMA signals is presented. Applying the dynamic error system analysis (DESA) method, it is proved that the proposed self-tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example shows its effectiveness. © The authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.},
author_keywords={ARMA signal;  Coloured noises;  Convergence analysis;  Identification;  Multisensor information fusion;  Selftuning Kalman filter},
document_type={Article},
source={Scopus},
}

@ARTICLE{Pang20154346,
author={Pang, C. and Sun, S.},
title={Fusion predictors for multisensor stochastic uncertain systems with missing measurements and unknown measurement disturbances},
journal={IEEE Sensors Journal},
year={2015},
volume={15},
number={8},
pages={4346-4354},
doi={10.1109/JSEN.2015.2416511},
art_number={7084585},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933558274&doi=10.1109%2fJSEN.2015.2416511&partnerID=40&md5=18587485a3488ee95dbbdbd52fe4676b},
affiliation={School of Electronic Engineering, Heilongjiang University, Harbin, China},
abstract={This paper addresses the information fusion state estimation problem for multisensor stochastic uncertain systems with missing measurements and unknown measurement disturbances. The missing measurements of sensors are described by Bernoulli distributed random variables. Measurements of sensors are subject to external disturbances whose any prior information is unknown. Stochastic parameter uncertainties of systems are depicted by multiplicative noises. For such complex systems with multiple sensors, the Kalman-like centralized fusion and distributed fusion state one-step predictors (i.e., prior filters) independent of unknown measurement disturbances are designed based on the linear unbiased minimum variance criterion, respectively. Estimation error cross-covariance matrices between any two local predictors are derived. Their steady-state properties are analyzed. The sufficient conditions for the existence of the steady-state predictors are given. Two simulation examples show the effectiveness of the proposed algorithms. © 2001-2012 IEEE.},
author_keywords={Fusion predictor;  Linear unbiased minimum variance;  Missing measurement;  Multi-sensor;  Multiplicative noise;  Unknown disturbance},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kim2015823,
author={Kim, J.-H. and Starr, J.W. and Lattimer, B.Y.},
title={Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke},
journal={Fire Technology},
year={2015},
volume={51},
number={4},
pages={823-845},
doi={10.1007/s10694-014-0413-6},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931576676&doi=10.1007%2fs10694-014-0413-6&partnerID=40&md5=ad36676dd4d23e919e0f54ac6ab312d3},
affiliation={Department of Mechanical Engineering, Virginia Tech, 114 Randolph Hall, Blacksburg, VA, United States},
abstract={Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in making firefighting robots autonomous because the commonly used sensors for autonomous robot navigation do not perform well in fire smoke-filled environments where low visibility and high temperature are present. In order to overcome these limitations, a multi-spectral vision system was developed that uses sensor fusion between stereo thermal infrared (IR) vision and frequency modulated-continuous wave (FMCW) radar to locate objects through zero visibility smoke in real-time. In this system, the stereo IR vision was used to obtain 3-D information about the scene while the radar provided more accurate distances of objects in the field of view. Through globally matching radar objects with those in the 3-D image, the accuracy of the stereo IR vision map was updated removing the far-field inaccuracy of the stereo IR as well as ghost objects created due to stereo mismatch. The system was sufficiently fast to provide real-time matching of objects in the scene allowing for dynamic reaction object tracking and locating. Through large-scale fire experiments with and without smoke in the field of view, the distance error for the stereo IR vision was reduced from 1% to 19.0% to 1% to 2% due to sensor fusion of the stereo IR with FMCW radar. © 2014, Springer Science+Business Media New York.},
author_keywords={Dense smoke;  Fire environment;  Firefighting robot;  FMCW radar;  Multisensor fusion;  Object detection;  Stereo IR vision},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sprager20151486,
author={Sprager, S. and Juric, M.B.},
title={An Efficient HOS-Based Gait Authentication of Accelerometer Data},
journal={IEEE Transactions on Information Forensics and Security},
year={2015},
volume={10},
number={7},
pages={1486-1498},
doi={10.1109/TIFS.2015.2415753},
art_number={7065314},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940993612&doi=10.1109%2fTIFS.2015.2415753&partnerID=40&md5=6db3e88c8b834215c29687c895e56a9b},
affiliation={Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia},
abstract={We propose a novel efficient and reliable gait authentication approach. It is based on the analysis of accelerometer signals using higher order statistics. Gait patterns are obtained by transformation of acceleration data in feature space represented with higher order cumulants. The proposed approach is able to operate on multichannel and multisensor data by combining feature-level and sensor-level fusion. Evaluation of the proposed approach was performed using the largest currently available data set OU-ISIR containing inertial data of 744 subjects. Authentication was performed by cross-comparison of gallery and probe gait patterns transformed in feature space. In addition, the proposed approach was evaluated using data set collected by McGill University, containing long-sequence acceleration signals of 20 subjects acquired by smartphone during casual walking. The results have shown an average equal error rate of 6% to 12%, depending on the selected experimental parameters and setup. When compared with the latest state of the art, evaluated performance reveal the proposed approach as one of the most efficient and reliable of the currently available accelerometer-based gait authentication approaches. © 2015 IEEE.},
author_keywords={accelerometer;  Gait analysis;  gait authentication;  higher-order cumulants;  higher-order statistics;  inertial sensors},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chen201514328,
author={Chen, Z. and Zhang, F. and Qu, X. and Liang, B.},
title={Fast measurement and reconstruction of large workpieces with freeform surfaces by combining local scanning and global position data},
journal={Sensors (Switzerland)},
year={2015},
volume={15},
number={6},
pages={14328-14344},
doi={10.3390/s150614328},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935860521&doi=10.3390%2fs150614328&partnerID=40&md5=c8d16e5ddd5b9a2b06a39c10262884de},
affiliation={State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China},
abstract={In this paper, we propose a new approach for the measurement and reconstruction of large workpieces with freeform surfaces. The system consists of a handheld laser scanning sensor and a position sensor. The laser scanning sensor is used to acquire the surface and geometry information, and the position sensor is utilized to unify the scanning sensors into a global coordinate system. The measurement process includes data collection, multi-sensor data fusion and surface reconstruction. With the multi-sensor data fusion, errors accumulated during the image alignment and registration process are minimized, and the measuring precision is significantly improved. After the dense accurate acquisition of the three-dimensional (3-D) coordinates, the surface is reconstructed using a commercial software piece, based on the Non-Uniform Rational B-Splines (NURBS) surface. The system has been evaluated, both qualitatively and quantitatively, using reference measurements provided by a commercial laser scanning sensor. The method has been applied for the reconstruction of a large gear rim and the accuracy is up to 0.0963 mm. The results prove that this new combined method is promising for measuring and reconstructing the large-scale objects with complex surface geometry. Compared with reported methods of large-scale shape measurement, it owns high freedom in motion, high precision and high measurement speed in a wide measurement range. © 2015 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={3-D reconstruction;  Combined measurement;  Large-scale workpieces with freeform surfaces;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kim2015503,
author={Kim, K.a  and Seol, S.b  and Kong, S.-H.c },
title={High-speed train navigation system based on multi-sensor data fusion and map matching algorithm},
journal={International Journal of Control, Automation and Systems},
year={2015},
volume={13},
number={3},
pages={503-512},
doi={10.1007/s12555-014-0251-9},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930090020&doi=10.1007%2fs12555-014-0251-9&partnerID=40&md5=77ff35e025e8080eb29ac2df4989ed88},
affiliation={LIG Nex1. Inc., Seongnam, South Korea; Agency for Defense Development (ADD), Daejeon, South Korea; CCS Graduate School for Green Transportation, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea},
abstract={Navigation system for high-speed trains is necessary for increased operational safety and efficiency, new services for customers, and low maintenance cost. This paper proposes a high accuracy navigation system for high-speed trains based on a sensor fusion algorithm, with non-holonomic constraints, for multiple sensors, such as accelerometers, gyroscopes, tachometers, Doppler radar, differential GPS, and RFID, and a map matching algorithm. In the proposed system, we consider the federated Kalman filter for sensor fusion, where local filters utilize filter models developed for various sensor types. Especially, the local Kalman filter for RFID positioning, that is detected at irregular time intervals due to the varying train speed and RFID tag spacing, is developed to maintain high performance during GPS outage. In addition, an orthogonal projection map matching algorithm is developed to improve the performance of the proposed system. The performance of the proposed system is demonstrated with numerous simulations for a high-speed train in Korea. The simulation results are analyzed with respect to the existence of tunnel, RFID deployment spacing, RFID location uncertainty, and DGPS error. © 2015, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg.},
author_keywords={Federated Kalman filter;  map matching;  multisensor fusion;  train navigation system},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chen201510526,
author={Chen, Y. and Zhao, Q.},
title={A novel square-root cubature informationweighted consensus filter algorithm for multi-target tracking in distributed camera networks},
journal={Sensors (Switzerland)},
year={2015},
volume={15},
number={5},
pages={10526-10546},
doi={10.3390/s150510526},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929376471&doi=10.3390%2fs150510526&partnerID=40&md5=8582c208d7fae5fa9cef99409682747d},
affiliation={Beijing Key Laboratory of Intelligence Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China},
abstract={This paper deals with the problem of multi-target tracking in a distributed camera network using the square-root cubature information filter (SCIF). SCIF is an efficient and robust nonlinear filter for multi-sensor data fusion. In camera networks, multiple cameras are arranged in a dispersed manner to cover a large area, and the target may appear in the blind area due to the limited field of view (FOV). Besides, each camera might receive noisy measurements. To overcome these problems, this paper proposes a novel multi-target square-root cubature information weighted consensus filter (MTSCF), which reduces the effect of clutter or spurious measurements using joint probabilistic data association (JPDA) and proper weights on the information matrix and information vector. The simulation results show that the proposed algorithm can efficiently track multiple targets in camera networks and is obviously better in terms of accuracy and stability than conventional multi-target tracking algorithms. © 2015 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Consensus algorithm;  Cubature Kalman filter;  Distributed camera networks;  Information filter;  Multi-target tracking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Pallauf20151284,
author={Pallauf, J. and Wagner, J. and Leon, F.P.},
title={Evaluation of state-dependent pedestrian tracking based on finite sets},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2015},
volume={64},
number={5},
pages={1284-1292},
doi={10.1109/TIM.2014.2375712},
art_number={6994874},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926666817&doi=10.1109%2fTIM.2014.2375712&partnerID=40&md5=fae135d616787fe6eddcc510e4425e0b},
affiliation={Faculty of Electrical Engineering and Information Technology, Institute of Industrial Information Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany},
abstract={This paper addresses the use of multiobject filters based on finite set statistics with a special focus on sensor characteristics for the use in distributed indoor pedestrian tracking with multiple sensors. For this purpose, a sensing framework is presented consisting of Lidar sensors together with depth cameras. To make use of previously existing knowledge about the measuring process, an adaptive sensor model is presented with a focus on state-dependent modeling of the sensor characteristics. The model incorporates changes in the probability of detection due to the distance between the object and the sensor, occlusions, and the sensor location-dependent environment. The model is incorporated into three approximations of a Bayes optimal multiobject filter, namely, the probability hypothesis density filter, the cardinalized probability hypothesis filter, and the cardinality balanced multitarget multi-Bernoulli filter. Using labeled sequential Monte Carlo implementations, the filters are evaluated with and without the proposed adaptive sensor models for partly simulated and real data. The evaluation was done using ground truth data obtained by a marker-based motion capture system. It is shown that the adaptive model achieves superior tracking results in simulation and real experiments for all approximations with the cardinalized probability hypothesis filter producing the best results. © 1963-2012 IEEE.},
author_keywords={Measurement;  multisensor systems;  signal processing;  state estimation;  tracking filters.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Aly2015563,
author={Aly, H. and Ismail, M.A.},
title={UbiMonitor: Intelligent fusion of body-worn sensors for real-time human activity recognition},
journal={Proceedings of the ACM Symposium on Applied Computing},
year={2015},
volume={13-17-April-2015},
pages={563-568},
doi={10.1145/2695664.2695912},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955513388&doi=10.1145%2f2695664.2695912&partnerID=40&md5=46bbacaf8b50d1c279cdab6940c2d76b},
affiliation={Computer and Systems Engineering, Dept. Alexandria University, Egypt},
abstract={Real-time monitoring of the human daily activities using wearable sensors has attracted lots of attention from researchers due to its crucial role in emerging fields such as pervasive healthcare and ambient assisted living. However, despite being an active field, recent benchmarks show a difficulty in providing robust monitoring of the different various activities under realistic everyday life conditions. In this paper, based on an in-depth understanding of the nature of 3D accelerometers and the physical properties of the different daily activities, we propose ubiMonitor as an accurate real-time activity monitor using low-cost off-theshelf three body-worn 3D accelerometers. For a robust monitoring, ubiMonitor (1) intelligently fuses the accelerometers and the acceleration in each of the three axes to provide key discriminative features for the different activities, (2) employs a novel hierarchical activity recognition scheme, and (3) applies an effective postprocessing stage to remove falsely detected activities and enhance the overall system accuracy. Experimental results using real traces from different eight subjects show that ubiMonitor can achieve an overall accuracy more than 95% with a median latency less than 3 msec. This is better than state-of-the-art by 23.4% in the recognition accuracy with a reduction of 70% in the sensors used. Copyright 2015 ACM.},
author_keywords={Activity recognition;  Ambient assisted living;  Multiclass classification;  Multisensor fusion;  Wearable sensors},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Khaydukova20151088,
author={Khaydukova, M.a  b  and Cetó, X.c  and Kirsanov, D.a  b  and del Valle, M.c  and Legin, A.a  b },
title={A Tool for General Quality Assessment of Black Tea—Retail Price Prediction by an Electronic Tongue},
journal={Food Analytical Methods},
year={2015},
volume={8},
number={5},
pages={1088-1092},
doi={10.1007/s12161-014-9979-3},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938084016&doi=10.1007%2fs12161-014-9979-3&partnerID=40&md5=299a60adfa542ebfd68db5c11a8d9312},
affiliation={Laboratory of Chemical Sensors, Institute of Chemistry, St. Petersburg State University, 7-9, Universitetskaya nab., St. Petersburg, Russian Federation; Laboratory of Artificial Sensory Systems, ITMO University, 49, Kronverkskiy pr., St. Petersburg, Russian Federation; Sensors & Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, Bellaterra, Spain},
abstract={Retail price of food products is a complex interplay between multiple factors. Overall product quality has got one of the most serious impacts on the price in many situations. In the present study, an artificial sensory system (potentiometric electronic tongue) was employed for the analysis of black tea samples purchased in the retail stores in Spain and Russia. It was possible to relate the response of a potentiometric sensor system with retail prices of various black tea samples by means of partial least squares (PLS) regression. PLS regression models are allowed for the prediction of retail price with mean relative errors of about 15 and 25 % for Spain’s tea bags and for loose-packed tea from Russia, respectively. The suggested approach shows a promise for the development of an instrumental analytical technique for regulatory authorities to fight with counterfeits and for commercial purposes to evaluate market space. © 2014, Springer Science+Business Media New York.},
author_keywords={Black tea;  Electronic tongue;  Multisensor system;  Retail price assessment},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Turolla20151155,
author={Turolla, A. and Marchesotti, L. and Regazzoni, C.},
title={A multisensor system for Ambient Intelligence applications},
journal={European Signal Processing Conference},
year={2015},
volume={06-10-September-2004},
pages={1155-1158},
art_number={7080171},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979900683&partnerID=40&md5=5386e4e78ed5612e9abddc1133240ffe},
affiliation={DIBE-University of Genova, Via dell'Opera Pia 11, Genova, Italy},
abstract={In this paper a complete architecture for Ambient Intelligence applications is presented. In particular the role of Data Fusion is shown to be performing to integrate heterogeneous information at different levels of abstraction. An Event Analysis method is presented in order to classify human activities within environments of interest whereas a location estimation method based on visual data is described in terms of logic functional architecture. © 2004 EUSIPCO.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Liu20157933,
author={Liu, B. and Zhang, F. and Qu, X.},
title={A method for improving the pose accuracy of a robot manipulator based on multi-sensor combined measurement and data fusion},
journal={Sensors (Switzerland)},
year={2015},
volume={15},
number={4},
pages={7933-7952},
doi={10.3390/s150407933},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926380537&doi=10.3390%2fs150407933&partnerID=40&md5=f0d420a553790196b6ce54875325af83},
affiliation={State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China},
abstract={An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%~78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0:8 × 1 ~ 2 × 0:8 × 1 m in the field of view (FOV) is indicated by the experimental results. © 2015 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Multi-sensor data fusion;  Pose accuracy;  Series robot;  Visual sensor},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2015507,
author={Wang, L.a  and Liu, M.b  and Meng, M.Q.-H.a },
title={Real-Time Multisensor Data Retrieval for Cloud Robotic Systems},
journal={IEEE Transactions on Automation Science and Engineering},
year={2015},
volume={12},
number={2},
pages={507-518},
doi={10.1109/TASE.2015.2408634},
art_number={7060735},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927668664&doi=10.1109%2fTASE.2015.2408634&partnerID=40&md5=94bba9bf5b42e55bbc51988a86135aa7},
affiliation={Department of Electronic Engineering, Chinese University of Hong Kong, Kowloon, Hong Kong; Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong},
abstract={Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios. © 2004-2012 IEEE.},
author_keywords={Cloud robotic system;  multisensor fusion;  real-time data retrieval},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yuan2015618,
author={Yuan, K.a  b  and Wang, Q.a  and Wang, L.a },
title={Fuzzy-logic-based terrain identification with multisensor fusion for transtibial amputees},
journal={IEEE/ASME Transactions on Mechatronics},
year={2015},
volume={20},
number={2},
pages={618-630},
doi={10.1109/TMECH.2014.2309708},
art_number={6775332},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908398536&doi=10.1109%2fTMECH.2014.2309708&partnerID=40&md5=ea56ada21ba6fb790ed28eabcfa57930},
affiliation={Intelligent Control Laboratory, College of Engineering, Peking University, Beijing, China; Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing, China},
abstract={Terrain identification is essential for the control of robotic transtibial prostheses to realize smooth locomotion transitions. In this paper, we present a real-time fuzzy-logic-based terrain identification method with multisensor fusion. Five locomotion features, including the foot inclination angle at the first strike, the shank inclination angle at the first strike, foot strike sequence, the foot inclination angle at mid-stance, and the shank inclination angle at toe-off, are used to identify different terrains and terrain transitions. These features are measured by the fusion of two triaxis gyroscopes, two triaxis accelerometers, two force sensitive resistors, and a timer, which can be embedded into the prosthesis. Based on these features, a fuzzy-logic-based identification method is proposed to identify five terrains: level ground, stair ascent, stair descent, ramp ascent, and ramp descent. Moreover, a transition constraint function is developed to improve the identification performance. The execution time of the identification method is 0.79 ms pm, 0.02 ms (mean pm standard error of mean) and continuous terrain identification results show that the method can be operated online in real time. The average identification accuracy of 98.74% pm 0.32% is obtained from experiments with six able-bodied and three amputee subjects during steady locomotion periods (no terrain transition). In locomotion transition periods, all the eight transitions we studied are correctly identified and the average identification delay is 9.06% pm 3.46% of one gait cycle. © 2014 IEEE.},
author_keywords={Fuzzy logic;  locomotion transitions;  multisensor fusion;  terrain identification;  transtibial prostheses},
document_type={Article},
source={Scopus},
}

@ARTICLE{Atia20151279,
author={Atia, M.M.a  and Liu, S.b  and Nematallah, H.c  and Karamat, T.B.a  and Noureldin, A.a },
title={Integrated indoor navigation system for ground vehicles with automatic 3-D alignment and position initialization},
journal={IEEE Transactions on Vehicular Technology},
year={2015},
volume={64},
number={4},
pages={1279-1292},
doi={10.1109/TVT.2015.2397004},
art_number={7027835},
note={cited By 15},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928023593&doi=10.1109%2fTVT.2015.2397004&partnerID=40&md5=ab2fbcb9bd90711e263e5eb25a72c2e4},
affiliation={Department of Electrical and Computer Engineering, Royal Military College (RMC), Kingston, ON, Canada; College of Automation, Harbin Engineering University, Harbin, China; Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada},
abstract={This paper introduces an autonomous integrated indoor navigation system for ground vehicles that fuses inertial sensors, light detection and ranging (LiDAR) sensors, received signal strength (RSS) observations in wireless local area networks (WLANs), odometry, and predefined occupancy floor maps. This paper proposes a solution for the problem of automatic self-alignment and position initialization indoors under the absence of an absolute navigation system such as Global Navigation Satellite Systems (GNSS). The initial tilt angles (roll and pitch) are estimated by an extended Kalman filter (EKF) that uses two horizontal accelerometers as measurements. The initial position and heading estimation is performed using a subimage matching algorithm based on normalized cross-correlation between projected 2-D LiDAR scans and an occupancy floor map of the environment. The ambiguities in position/heading initialization are resolved using RSS. The proposed position/heading estimation module is also utilized in navigation mode as a source of absolute position/heading updates to EKF for enhanced observability. The state predictor is an enhanced 3-D inertial navigation system that utilizes low-cost microelectromechanical system (MEMS)-based reduced inertial sensor set aided by vehicle odometry. In navigation mode, LiDAR scans are used to estimate the vehicle's relative motions using an inertial-aided iterative closest point algorithm. To fuse all available measurements, a multirate multimode EKF design is proposed to correct navigation states and estimate sensor biases. The developed system was tested under a real indoor office environment covered by an IEEE 802.11 WLAN on a mobile robot platform equipped with MEMS inertial sensors, a WLAN interface, a 2-D LiDAR scanner, and a quadrature encoder. Results demonstrated the capabilities of the self-alignment and initialization module and showed average submeter-level positioning accuracy. © 1967-2012 IEEE.},
author_keywords={Extended Kalman Filter;  Indoor Navigation;  LiDAR-Aided Navigation;  Map-Aided Navigation;  MEMS-based Inertial Sensors;  Mobile Robots;  Multisensor Fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Moradi20151437,
author={Moradi, M. and Sivoththaman, S.},
title={MEMS multisensor intelligent damage detection for wind turbines},
journal={IEEE Sensors Journal},
year={2015},
volume={15},
number={3},
pages={1437-1444},
doi={10.1109/JSEN.2014.2362411},
art_number={6918394},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919782649&doi=10.1109%2fJSEN.2014.2362411&partnerID=40&md5=ecbf48041a0359f72413653f3fc289d6},
affiliation={Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada},
abstract={Maintenance and repair of wind turbine structures have become more challenging and at the same time essential as they evolve into larger dimensions or located in places with limited access. Even small structural damages may invoke catastrophic detriment to the integrity of the system. So, cost-effective, predictive, and reliable structural health monitoring (SHM) system has been always desirable for wind turbines. A real-time nondestructive SHM technique based on multisensor data fusion is proposed in this paper. The objective is to critically analyze and evaluate the feasibility of the proposed technique to identify and localize damages in wind turbine blades. The structural properties of the turbine blade before and after damage are investigated through different sets of finite-element method simulations. Based on the obtained results, it is shown that information from smart sensors, measuring strains, and vibrations data, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine these two diagnostic tools to improve the detection system that provides a more robust reading with reduced false alarms. © 2014 IEEE.},
author_keywords={Condition monitoring;  data fusion;  microelectromechanical systems;  strain sensor;  vibration},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shamshirband2015850,
author={Shamshirband, S.a  and Petkovic, D.b  and Javidnia, H.c  and Gani, A.a },
title={Sensor data fusion by support vector regression methodology - A comparative study},
journal={IEEE Sensors Journal},
year={2015},
volume={15},
number={2},
pages={850-854},
doi={10.1109/JSEN.2014.2356501},
art_number={6894593},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915746827&doi=10.1109%2fJSEN.2014.2356501&partnerID=40&md5=0cfbed21fa9746a40d1d74650b5aba19},
affiliation={Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia; Department for Mechatronics and Control, Faculty of Mechanical Engineering, University of Niš, Niš, Serbia; Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran},
abstract={Multisensor data fusion can be considered as a strong nonlinear system. A precise analytical solution is challenging to obtain, thus making it hard to dissect with routine diagnostic systems. Since tried-and-true logical systems are extremely difficult to undertake, soft computing methodologies are deemed having potential for such applications. This paper presents the support vector regression (SVR) methodology for sensor fusion to improve tracking ability. Radial basis function (RBF) and polynomial function are used as SVR kernel functions. The system combines Kalman filtering and soft computing principle, i.e., SVR, to structure an effective information combination method for the target framework. A radar-infrared system is proposed to adapt contextual changes and lessen the dubious unsettling influence of an information estimation from multisensory data. The experimental results show that an improvement in predictive accuracy and generalization capability can be achieved using the SVR with RBF kernel compared with the SVR with polynomial kernel approach. © 2001-2012 IEEE.},
author_keywords={contextual information;  Sensor fusion;  soft computing;  support vector regression},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Wichit201554,
author={Wichit, N.},
title={Multisensor data fusion model for activity detection},
journal={International Conference on ICT and Knowledge Engineering},
year={2015},
volume={2015-January},
number={January},
pages={54-59},
doi={10.1109/ICTKE.2014.7001535},
art_number={7001535},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936942311&doi=10.1109%2fICTKE.2014.7001535&partnerID=40&md5=e4c2f24696a376bc0a6aa7a94bc7a9fe},
affiliation={Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University Hat Yai, Songkhla, Thailand},
abstract={This paper describes a novel system for detecting human activity based on multi-sensor approach. This approach aim to provide accuracy and robustness to the activity recognition system. The performances of the systems that fuse multiple data coming from different sources are deemed to benefit from the heterogeneity and the diversity of the information involved. In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. This applies an information fusion algorithm based Fuzzy Logic with a set of rules in order to recognize Human Behavior. So as to obtain Feature-level Fusion from Extraction of physical data. © 2014 IEEE.},
author_keywords={Human Activity Detection;  Information fusion;  Multi-sensor data Fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zhao2015238,
author={Zhao, Y.},
title={A method of multi-sensor data fusion based on rough set theory and ART-2 neural network},
journal={Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014},
year={2015},
volume={1},
pages={238-240},
doi={10.1109/ISCID.2014.48},
art_number={7064181},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931076303&doi=10.1109%2fISCID.2014.48&partnerID=40&md5=7b45126101b7fcdb5247db26b5e5d5e9},
affiliation={Department of Electrical Engineering, Shandong Polytechnic, Jinan, China},
abstract={ART-2 is a self-organized and unsupervised artificial neural network which can be used to deal with data fusion. But we found that the problem of data overloading is hard to be solved in the process of data fusion. Rough set is a mathematical approach to deal with vague, uncertain and imperfect data. So, we proposed a method of multi-sensor data fusion based on rough set theory and ART-2 neural network. At last, the simulation results show the feasibility and the validity of the proposed fusion system © 2014 IEEE.},
author_keywords={ART-2 Neural Network;  Multi-sensor Data Fusion;  Rough Set Theory},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wang20154145,
author={Wang, H.a  and Huang, Z.a  b  and Zhong, N.a  c  and Huang, J.a  and Han, Y.d },
title={Using multi-sensor data fusion in outlier detection of building strain monitoring data},
journal={Journal of Information and Computational Science},
year={2015},
volume={12},
number={11},
pages={4145-4152},
doi={10.12733/jics20106331},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939452473&doi=10.12733%2fjics20106331&partnerID=40&md5=b976d8cc4109e4b43dbee5dc79887b2e},
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; National Center for Quality Supervision and Test of Building Engineering, China Academy of Building Research, Beijing, China},
abstract={More and more sensor-based information systems have been put into use, and the data from the sensors are growing rapidly. The emergence of multi-sensor data fusion provides more mining methods and can solve the problem from many different viewpoints. This paper presents an improved outlier detection method for the building strain monitoring data by using multi-sensor data fusion. According to the real-time temperature data, the strain data at the same period can be segmented. In each segment the traditional outlier detection is used. Comparing the segment with the non-segment method, it is proved that the method using multi-sensor data fusion improves the efficiency of outlier detection. ©, 2015, Binary Information Press. All right reserved.},
author_keywords={Data fusion;  Monitoring system;  Multi-sensor;  Outlier detection},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jiang2015340,
author={Jiang, X. and Xue, H. and Wang, C. and Zhou, Y.},
title={Research of data fusion method of multi-sensor based on correlation coefficient of confidence distance},
journal={Open Cybernetics and Systemics Journal},
year={2015},
volume={9},
pages={340-344},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964509967&partnerID=40&md5=12446f5d7d7878d7d547f9f0f7794667},
affiliation={College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China},
abstract={Because of the sensor or environment problem, the multi-sensor detection data can result in data mutation for the individual sensor. To obtain consistent, stable and accurate tested target description, the paper adopts confidence distance matrix, distance correlation coefficient and p-value significant test to get The number of optimal fusion number of multi-sensor, and uses maximum likelihood estimation to study the multi-sensor data fusion. The results show that the algorithm mentioned by the paper is superior to the multi-sensor consistency data fusion means and is fairly practical. © 2015, Jiang et al.; Licensee Bentham Open. All rights reserved.},
author_keywords={Confidence distance;  Correlation coefficient;  Maximum likelihood estimation;  P-value test;  Support},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li2015,
author={Li, G. and Qin, D. and Ju, H.},
title={Localization of Wheeled Mobile Robot Based on Extended Kalman Filtering},
journal={MATEC Web of Conferences},
year={2015},
volume={22},
doi={10.1051/matecconf/20152201061},
art_number={01061},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943807272&doi=10.1051%2fmatecconf%2f20152201061&partnerID=40&md5=6e9bdc85b23abcb7d93937f4e2053df2},
affiliation={Chengdu University of Information Technology, Chengdu, Sichuan, China},
abstract={A mobile robot localization method which combines relative positioning with absolute orientation is presented. The code salver and gyroscope are used for relative positioning, and the laser radar is used to detect absolute orientation. In this paper, we established environmental map, multi-sensor information fusion model, sensors and robot motion model. The Extended Kalman Filtering (EKF) is adopted as multi-sensor data fusion technology to realize the precise localization of wheeled mobile robot. © Owned by the authors, published by EDP Sciences, 2015.},
author_keywords={EKF;  Multi-sensor;  Positioning;  Wheeled mobile robot},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ozdemir2015,
author={Ozdemir, O.a  and Choi, S.b  and Allen, T.G.a  and Varshney, P.K.b },
title={Uncertainty characterization using copulas for classification},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9498},
doi={10.1117/12.2181908},
art_number={94980A},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938825439&doi=10.1117%2f12.2181908&partnerID=40&md5=d9a9a4f95627c3e3cdb42ec6a8d7105b},
affiliation={Boston Fusion Corp., 1 Van de Graaff Drive, Burlington, MA, United States; Dept. of EECS, Syracuse University, Syracuse, NY, United States},
abstract={We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers. © 2015 SPIE.},
author_keywords={classification;  copula theory;  Multisource data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wang2015,
author={Wang, H.a  and Dong, S.a  b },
title={Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion},
journal={Mathematical Problems in Engineering},
year={2015},
volume={2015},
doi={10.1155/2015/854085},
art_number={854085},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949255659&doi=10.1155%2f2015%2f854085&partnerID=40&md5=1874bd956e52c980629859f94a0e6a13},
affiliation={School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, China; School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China},
abstract={In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-Time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone. © 2015 Huadong Wang and Shi Dong.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Völker201554,
author={Völker, C.a  and Shokouhi, P.b },
title={Multi sensor data fusion approach for automatic honeycomb detection in concrete},
journal={NDT and E International},
year={2015},
volume={71},
pages={54-60},
doi={10.1016/j.ndteint.2015.01.003},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922383852&doi=10.1016%2fj.ndteint.2015.01.003&partnerID=40&md5=592913a3763c08f96171774ac8aa8309},
affiliation={BAM Federal Institute for Materials Research and Testing, Unter Den Eichen 87, Berlin, Germany; Department of Civil and Environmental Engineering, Pennsylvania State University, 215 Sackett Bldg., University Park, PA, United States},
abstract={We present a systematic approach for fusion of multi-sensory nondestructive testing data. Our data set consists of impact-echo, ultrasonic pulse echo and ground penetrating radar data collected on a large-scale concrete specimen with built-in honeycombing defects. From each data set, the most significant signatures of honeycombs were extracted in the form of features. We applied two simple data fusion algorithms to the data: Dempster's rule of combination and the Hadamard product. The performance of the fusion rules versus the single-sensor testing was evaluated. The fusion rules exhibit a slight improvement of false alarm rate over the best single sensor. ©2015 Elsevier Ltd. All rights reserved.},
author_keywords={Concrete evaluation;  Data fusion;  Honeycombing},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jia201522,
author={Jia, R.-S.a  b  and Liu, C.a  and Sun, H.-M.a  and Yan, X.-H.a },
title={A situation assessment method for rock burst based on multi-agent information fusion},
journal={Computers and Electrical Engineering},
year={2015},
volume={45},
pages={22-32},
doi={10.1016/j.compeleceng.2015.04.015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942315885&doi=10.1016%2fj.compeleceng.2015.04.015&partnerID=40&md5=231af9ffeb276e63524a8217e07feef2},
affiliation={College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China; State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province, Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, China},
abstract={An assessment approach to assess the likelihood of rock burst in coal mines by integrating the Multi-Agent System with data fusion techniques is proposed in this paper. We discuss an optimal algorithm for multi-sensor data fusion to improve the accuracy and reliability of the source data. Some model-based situation quantization methods are described and a rock burst situation quantitative assessment model incorporating improved Dempster-Shafer theory is presented. The Auto-Regressive, Moving Average and Holt-Winters models are used to address indefinability and inaccuracy of the prediction. A case study demonstrates that the proposed situation assessment model is capable of producing relatively accurate forecasts, and thereby it can provide coal mine decision-makers with an overview of the development of rock bursts. © 2014 Elsevier Ltd. All rights reserved.},
author_keywords={Forecasting model;  Information fusion;  Multi-agent system;  Rock burst;  Situation assessment},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Sokolnikov2015,
author={Sokolnikov, A.},
title={Multisensor fusion with the ramification algorithm},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2015},
volume={9476},
doi={10.1117/12.2180359},
art_number={94760X},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948678835&doi=10.1117%2f12.2180359&partnerID=40&md5=34c6871462e70a05c884c863d1682337},
affiliation={Visual Solutions and Applications, South Bend, IN, United States},
abstract={The present approach combines data fusion from several sensor types to enhance the overall detection and classification performance. The fusion of different sensors is implemented at data and feature levels that results in enhanced target identification by the means of spatial spectral analysis. © 2015 SPIE.},
author_keywords={Adaptive modeling;  Automatic target recognition;  Multisensor fusion;  Prediction modeling;  Ramification algorithm;  Tree-based algorithm},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Alexiev201599,
author={Alexiev, K. and Shishkov, G. and Popova, N.},
title={Human activity registration using multisensor data fusion},
journal={Cybernetics and Information Technologies},
year={2015},
volume={15},
number={7},
pages={99-108},
doi={10.1515/cait-2015-0093},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953402107&doi=10.1515%2fcait-2015-0093&partnerID=40&md5=3b534dd8b63b55152a0c77648915f4cc},
affiliation={Institute of Information and Communication Technologies, Bulgarian Acad. of Sci., Sofia, Bulgaria},

license_type: permissive

extension: bib