text:
, China},
abstract={An airborne distributed position and orientation system (POS) is high-precision measurement equipment that can accurately provide multinode time-spatial reference for novel remote sensing system as multitask imaging sensors and array antenna synthetic aperture radar. However, it is difficult for multisensor to precisely acquire information at the same moment and result in data fusion error. Thus, the measurement precision is severely degraded. To solve the problem, a multisensor time synchronization error modeling and compensation method is proposed. Based on the component and operation principles of distributed POS, the time synchronization mechanism is analyzed. Multisensor time synchronization error models that include time delay error, random error, and time-varying error are established. A time synchronization error compensation method of the distributed POS is proposed. The experiment results show that the proposed method can accurately calibrate and compensate for the time synchronization error, and improve the measurement precision of the distributed POS. It verified the validity of the proposed method. © 2016 IEEE.},
author_keywords={Distributed position and orientation system (POS);  modeling and compensation;  multisensor;  time synchronization error},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dou2016,
author={Dou, Q.a  and Wei, L.a  and Magee, D.R.a  and Atkins, P.R.b  and Chapman, D.N.c  and Curioni, G.c  and Goddard, K.F.d  and Hayati, F.b  and Jenks, H.e  and Metje, N.c  and Muggleton, J.f  and Pennock, S.R.e  and Rustighi, E.f  and Swingler, S.G.d  and Rogers, C.D.F.c  and Cohn, A.G.a },
title={3D buried utility location using a marching-cross-section algorithm for multi-sensor data fusion},
journal={Sensors (Switzerland)},
year={2016},
volume={16},
number={11},
doi={10.3390/s16111827},
art_number={1827},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994761932&doi=10.3390%2fs16111827&partnerID=40&md5=7d92316c40efcb0d77e7b9722dd7c00a},
affiliation={School of Computing, University of Leeds, Leeds, United Kingdom; School of Electronic, Electrical and Computing Engineering, University of Birmingham, Birmingham, United Kingdom; School of Civil Engineering, University of Birmingham, United Kingdom; School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; School of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom; Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom},
abstract={We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post-and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation. © 2016 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Buried utility location;  Marching-cross-section algorithm;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cappello2016529,
author={Cappello, F. and Ramasamy, S. and Sabatini, R.},
title={A low-cost and high performance navigation system for small RPAS applications},
journal={Aerospace Science and Technology},
year={2016},
volume={58},
pages={529-545},
doi={10.1016/j.ast.2016.09.002},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992671877&doi=10.1016%2fj.ast.2016.09.002&partnerID=40&md5=64852c8a35545b305a4b05eb4a0d2986},
affiliation={School of Engineering – Aerospace Engineering and Aviation, RMIT University, Melbourne, Victoria, Australia},
abstract={Modern Remotely Piloted Aircraft Systems (RPAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced functionalities and trusted autonomy in a wide range of mission-essential and safety-critical tasks. In particular, Navigation and Guidance Systems (NGS) for small RPAS require a typical combination of lightweight, compact and inexpensive sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. In this paper, the synergies attainable by the combination of Global Navigation Satellite System (GNSS), Micro-Electromechanical System based Inertial Measurement Unit (MEMS-IMU) and Vision-Based Navigation (VBN) sensors are explored. In case of VBN, an appearance-based navigation technique is adopted and feature extraction/optical flow methods are employed to estimate the navigation parameters during precision approach and landing phases. A key novelty of the proposed approach is the employment of Aircraft Dynamics Models (ADM) augmentation to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. To obtain the best estimates of Position, Velocity and Attitude (PVA), different sensor combinations are analysed and dynamic Boolean Decision Logics (BDL) are implemented for data selection before the centralised data fusion is accomplished. Various alternatives for data fusion are investigated including a traditional Extended Kalman Filter (EKF) and a more advanced Unscented Kalman Filter (UKF). A novel hybrid controller employing fuzzy logic and Proportional–Integral–Derivative (PID) techniques is implemented to provide effective stabilisation and control of pitch and roll angles. After introducing the key mathematical models describing the three NGS architectures: EKF based VBN-IMU-GNSS (VIG) and VBN-IMU-GNSS-ADM (VIGA) and UKF based Enhanced VIGA (EVIGA), the system performances are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope. A dedicated ADM processor (i.e., a local pre-filter) is adopted in the EVIGA architecture to account for the RPAS manoeuvring envelope in different flight phases (assisted by a manoeuvre identification algorithm), in order to extend the ADM validity time across all segments of the RPAS trajectory. Simulation results show that the VIG, VIGA and EVIGA systems are compliant with ICAO requirements for precision approach down to CAT-II. In all other flight phases, the VIGA system shows improvement in PVA data output with respect to the VIG system. The EVIGA system shows the best performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved in this configuration. © 2016 Elsevier Masson SAS},
author_keywords={Aircraft Dynamics Model (ADM);  Global Navigation Satellite System (GNSS);  Kalman Filter;  Multi-Sensor Data Fusion (MSDF);  Remotely Piloted Aircraft Systems (RPAS);  Vision-Based Navigation (VBN)},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Yin20163640,
author={Yin, T.a  and Feret, J.-B.b  and Gastellu-Etchegorry, J.-P.a  and Lauret, N.a },
title={Data simulation and fusion of imaging spectrometer and LiDAR multi-sensor system through dart model},
journal={International Geoscience and Remote Sensing Symposium (IGARSS)},
year={2016},
volume={2016-November},
pages={3640-3643},
doi={10.1109/IGARSS.2016.7729943},
art_number={7729943},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007477086&doi=10.1109%2fIGARSS.2016.7729943&partnerID=40&md5=c80d15e25039505a4d315590775cd7f9},
affiliation={Centre DEtudes Spatiales de la BIOsphere - CNES, CNRS, IRD, Universite de Toulouse, 18 avenue. Edouard Belin, bpi 2801, Toulouse Cedex 9, France; IRSTEA, UMR-TETIS, 500 rue J.F. Breton, Montpellier Cedex 5, France},
abstract={Multi-sensor systems are increasingly demanding in recent remote sensing (RS) applications. Combination of LiDAR and imaging spectrometers is an emerging technique used by several recent airborne systems. The combined data provide both functional and structural information, which makes this technique a unique tool for understanding and management of the Earth's ecosystems. The rapid development of this technique demands the simulation and validation of the combined data. In this paper, we introduce a new method to simulate data fusion of multi-sensor system which combined LiDAR and imaging spectrometer, with any experimental, instrumental, and geometrical configurations of systems. This method is implemented in the latest release of discrete anisotropic radiative transfer (DART) model. © 2016 IEEE.},
author_keywords={DART;  fusion;  Imaging spectrometer;  LiDAR;  multisensor system;  radiative transfer},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Santhosh2016,
author={Santhosh, K.V. and Joy, B.},
title={Analysis of additive in a liquid level process using multi sensor data fusion},
journal={Proceedings of the 10th International Conference on Intelligent Systems and Control, ISCO 2016},
year={2016},
doi={10.1109/ISCO.2016.7726924},
art_number={7726924},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007375275&doi=10.1109%2fISCO.2016.7726924&partnerID=40&md5=80835077d7eb7d2a17a7c380502513a6},
affiliation={Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal, India},
abstract={This paper presents a technique for analyzing the amount of additives added to the liquid, whose level is to be controlled. The objective of the proposed work is to design a technique which will (i) measure level of liquid independent of solution, (ii) analyze the quantity of additives present in the solution and (iii) design a multi sensor framework to achieve objectives (i) and (ii). Capacitive Level Sensor (CLS) and Ultrasonic Level Sensor (ULS) are used for measurement of liquid level. Respective data conversion circuits are designed to convert output of both the sensor to a standard signal (voltage). For incorporation of Multi Sensor Data Fusion (MSDF) Joint Defense Labortory (JDL) framework is considered. Once the system is designed it is tested with data. Results obtained shows successful implementation of proposed work. © 2016 IEEE.},
author_keywords={Capacitance Level sensor;  JDL framework;  Liquid level measurement;  multisensor data fusion;  Ultrasonic level sensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhang20161347,
author={Zhang, C.a  and Guo, C.b },
title={Multi-sensor information fusion of a particle filter on the basis of a two-stage adaptive weight},
journal={Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University},
year={2016},
volume={37},
number={10},
pages={1347-1352},
doi={10.11990/jheu.201510074},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994410080&doi=10.11990%2fjheu.201510074&partnerID=40&md5=d29dc8a1e7b3e0b56c4e0fb0be056390},
affiliation={Navigation College, Dalian Maritime University, Dalian, China; School of Information Science and Technology, Dalian Maritime University, Dalian, China},
abstract={In order to solve the problems of low quality and degeneration of particles in the process of particle filtering, a particle filtering algorithm based on the two-stage adaptive weight was proposed. The algorithm was divided into two steps. First, multi-sensor data was sent to the appropriate particle filter calculation module, with optimizing particle distribution as a purpose, and the proposed distribution density was updated. Second, in the final particle filter module based on the adaptive weight, a complete likelihood function was structured. Meanwhile, by Euclidean distance and the accuracy factor reflecting the statistic properties of the measured noise, the distribution of the adaptive weight was adjusted to attain a more accurate estimate. A simulation experiment shows the effectiveness of the algorithm. © 2016, Editorial Department of Journal of HEU. All right reserved.},
author_keywords={Adaptive adjusted weight;  Information fusion;  Multisensor;  Particle filter;  Proposal density},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jia2016,
author={Jia, B.a  and Sun, T.b  and Xin, M.b },
title={Iterative diffusion-based distributed cubature gaussian mixture filter for multisensor estimation},
journal={Sensors (Switzerland)},
year={2016},
volume={16},
number={10},
doi={10.3390/s16101741},
art_number={1741},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992386818&doi=10.3390%2fs16101741&partnerID=40&md5=fe453941566fafc2215de7ccab1c706a},
affiliation={Intelligent Fusion Technology, Germantown, MD, United States; Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, United States},
abstract={In this paper, a distributed cubature Gaussian mixture filter (DCGMF) based on an iterative diffusion strategy (DCGMF-ID) is proposed for multisensor estimation and information fusion. The uncertainties are represented as Gaussian mixtures at each sensor node. A high-degree cubature Kalman filter provides accurate estimation of each Gaussian mixture component. An iterative diffusion scheme is utilized to fuse the mean and covariance of each Gaussian component obtained from each sensor node. The DCGMF-ID extends the conventional diffusion-based fusion strategy by using multiple iterative information exchanges among neighboring sensor nodes. The convergence property of the iterative diffusion is analyzed. In addition, it is shown that the convergence of the iterative diffusion can be interpreted from the information-theoretic perspective as minimization of the Kullback-Leibler divergence. The performance of the DCGMF-ID is compared with the DCGMF based on the average consensus (DCGMF-AC) and the DCGMF based on the iterative covariance intersection (DCGMF-ICI) via a maneuvering target-tracking problem using multiple sensors. The simulation results show that the DCGMF-ID has better performance than the DCGMF based on noniterative diffusion, which validates the benefit of iterative information exchanges. In addition, the DCGMF-ID outperforms the DCGMF-ICI and DCGMF-AC when the number of iterations is limited. © 2016 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Diffusion;  Distributed estimation;  Gaussian mixture;  Sensor networks},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ehlenbröker2016337,
author={Ehlenbröker, J.-F. and Mönks, U. and Lohweg, V.},
title={Sensor defect detection in multisensor information fusion},
journal={Journal of Sensors and Sensor Systems},
year={2016},
volume={5},
number={2},
pages={337-353},
doi={10.5194/jsss-5-337-2016},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992331719&doi=10.5194%2fjsss-5-337-2016&partnerID=40&md5=653e4995cce7596a41c8adea9c98823d},
affiliation={InIT-Institute Industrial IT, Langenbruch 6, Lemgo, Germany},
abstract={In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown. © 2016 The Author(s).},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Pereira20161381,
author={Pereira, C.B.a  and Yu, X.a  and Blazek, V.a  b  and Venema, B.a  and Leonhardt, S.a },
title={Multisensor data fusion for enhanced respiratory rate estimation in thermal videos},
journal={Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
year={2016},
volume={2016-October},
pages={1381-1384},
doi={10.1109/EMBC.2016.7590965},
art_number={7590965},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009069695&doi=10.1109%2fEMBC.2016.7590965&partnerID=40&md5=294432a5699b7e846f09f55b917f653f},
affiliation={Philips Medical Information Technology, RWTH Aachen University, Aachen, Germany; Czech Institute of Informatics, Robotics and Cybernetics, CTU Prague, Prague, Czech Republic},
abstract={Scientific studies have demonstrated that an atypical respiratory rate (RR) is frequently one of the earliest and major indicators of physiological distress. However, it is also described in the literature as 'the neglected vital parameter', mainly due to shortcomings of clinical available monitoring techniques, which require attachment of sensors to the patient's body. The current paper introduces a novel approach that uses multisensor data fusion for an enhanced RR estimation in thermal videos. It considers not only the temperature variation around nostrils and mouth, but the upward and downward movement of both shoulders. In order to analyze the performance of our approach, two experiments were carried out on five healthy candidates. While during phase A, the subjects breathed normally, during phase B they simulated different breathing patterns. Thoracic effort was the gold standard elected to validate our algorithm. Our results show an excellent agreement between infrared thermography (IRT) and ground truth. While in phase A a mean correlation of 0.983 and a root-mean-square error of 0.240 bpm (breaths per minute) was obtained, in phase B they hovered around 0.995 and 0.890 bpm, respectively. In sum, IRT may be a promising clinical alternative to conventional sensors. Additionally, multisensor data fusion contributes to an enhancement of RR estimation and robustness. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Elfring2016,
author={Elfring, J.a  and Appeldoorn, R.a  and van den Dries, S.b  and Kwakkernaat, M.a },
title={Effective world modeling: Multisensor data fusion methodology for automated driving},
journal={Sensors (Switzerland)},
year={2016},
volume={16},
number={10},
doi={10.3390/s16101668},
art_number={1668},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991687209&doi=10.3390%2fs16101668&partnerID=40&md5=8dcc2b99773b83006ee2e906e35e380a},
affiliation={Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond, Netherlands; Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands},
abstract={The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture. © 2016 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Automated driving;  Data fusion;  Multisensor;  World modeling},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Wang2016333,
author={Wang, Y. and Yue, W.},
title={Target data association in communication constrained environment using CART: Compressed adaptive reference topology},
journal={Proceedings of 2016 8th IEEE International Conference on Communication Software and Networks, ICCSN 2016},
year={2016},
pages={333-338},
doi={10.1109/ICCSN.2016.7586677},
art_number={7586677},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994499314&doi=10.1109%2fICCSN.2016.7586677&partnerID=40&md5=538ba2edf503cbfabf4014408bdf93e7},
affiliation={Department of Electronic Engineering, Tsinghua University, Beijing, China},
abstract={Target data association is one of the key procedures of multi-sensor data fusion and many algorithms have been developed to solve the traditional association problem. However, performance of traditional methods is degraded in communication constrained environment. In this paper, an approach is proposed to solve the problem of target data association in communication constrained environment. In the algorithm, we extract, quantify and compress the reference topology feature in order to complete the association with lower amount of data than traditional methods. Simulation results show that the proposed algorithm can achieve higher correct rate in target data association than traditional methods with the same data volume. © 2016 IEEE.},
author_keywords={communication constrianed environment;  data association;  reference topology;  sensor fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cheng2016,
author={Cheng, L.a  and Xing, X.b  and Xie, W.a  and Cheng, R.c  and Wang, L.c },
title={Design of Health Monitoring Program for Filling System based on Data Level Fusion},
journal={MATEC Web of Conferences},
year={2016},
volume={77},
doi={10.1051/matecconf/20167705001},
art_number={05001},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994000090&doi=10.1051%2fmatecconf%2f20167705001&partnerID=40&md5=bbb3cd549e47e184e98bcddd04c87eaf},
affiliation={1Department of Space Equipment, Equipment Academy, Beijing, China; Department of Equipment Acquisition, Equipment Academy, Beijing, China; Department of Graduate Management, Equipment Academy, Beijing, China},
abstract={Aiming at filling system health monitoring, the health monitoring type partition based on the data level fusion is studied. The health monitoring type is mainly divided into two parts, fusion-threshold monitoring based on single sensor data and fusion monitoring based on multi-sensor data of the same type. On this basis, Single sensor fusion monitoring based on RTS-TA algorithm and Multi-sensor fusion monitoring based on improved weighted fusion algorithm are designed. For multi-sensor data fusion, the fusion schemes are designed respectively according to the different situation whether the initial accuracy is known or not. © The Authors, published by EDP Sciences, 2016.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhan20162235,
author={Zhan, K. and Jiang, H. and Zhao, T.-Q. and Yu, Y.-Z.},
title={Multi-target joint tracking and classification based on model-class-matched PHD filter and TBM},
journal={Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics},
year={2016},
volume={38},
number={10},
pages={2235-2243},
doi={10.3969/j.issn.1001-506X.2016.10.03},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990853739&doi=10.3969%2fj.issn.1001-506X.2016.10.03&partnerID=40&md5=fb6edad09f6eb580ef2c7b51052ec437},
affiliation={Scool of Automation Science and Electrical Engineering, Beihang University, Beijing, China},
abstract={To solve the multi-target joint tracking and classification under clutter and miss detection, the model-class-matched probability hypothesis density (PHD) filter is proposed and combined with the transferable belief model (TBM) framework of multiple sensors, and it is recursively implemented by multiple kinematic radars and particle filtering. In our algorithm, the priori information of aircraft is estimated to replace the attribute sensor. At the prediction stage, the particles of the PHD filter are matched by model and class; the particle weights are updated after the sensors receive the measurement information; then, the targets' posterior state-model-class distribution is got by the corresponding weights of the particles; the targets' state and class can be obtained simultaneously by these PHD filters; finally, by integrating TBM and the track particle labeling algorithm, the fusion result of multiple sensors is acquired. Simulations indicate that the performance of the proposed model-class-matched PHD filter is much better than that of the traditional PHD filter, and in the meantime the TBM framework of multiple sensors also improves the performance the proposed algorithm. © 2016, Editorial Office of Systems Engineering and Electronics. All right reserved.},
author_keywords={Joint tracking and classification;  Multi-sensor data fusion;  Particle filtering;  Probability hypothesis density (PHD);  Transferable belief model (TBM)},
document_type={Article},
source={Scopus},
}

@ARTICLE{Park2016749,
author={Park, J.a  and Lee, K.b  and Sim, S.c  and Jung, H.d  and Spencer, B.F., Jr.e },
title={Traffic Safety Evaluation for Railway Bridges Using Expanded Multisensor Data Fusion},
journal={Computer-Aided Civil and Infrastructure Engineering},
year={2016},
volume={31},
number={10},
pages={749-760},
doi={10.1111/mice.12210},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984916602&doi=10.1111%2fmice.12210&partnerID=40&md5=360bae9d273b68683076d6de16593613},
affiliation={Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; High-Speed Railroad Systems Research Center, Korea Railroad Research Institute, Uiwang, South Korea; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)Ulsan, South Korea; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States},
abstract={The traffic safety of a railway bridge is generally evaluated by levels of structural responses such as acceleration, vertical displacement, and deck twist. Whereas acceleration can be readily measured in general, acquiring displacement and twist responses in field testing is often a challenging task due to lack of appropriate sensors. As most existing displacement transducers are designed to measure at a single location, the deck twist which is calculated from four displacements requires costly and labor-intensive sensor instrumentation. To effectively address the issue, this study proposes an integral strategy for the traffic safety evaluation of railway bridges using multisensor data. The proposed approach provides a formulation to estimate the dense displacement necessary for obtaining twist responses using acceleration and strain measurements. Wireless sensors are adopted because of their intrinsic advantages in multimetric sensing of heterogeneous data, convenient sensor instrumentation, and high-fidelity time-synchronized data acquisition. The proposed approach for dense displacement estimation is numerically and experimentally validated using beam models. Subsequently, a full-scale experiment on a railway bridge is conducted to evaluate the traffic safety for high-speed trains at three different speeds of 280 km/h, 300 km/h, and 400 km/h. The acceleration, vertical displacement, and twist are obtained and compared with design limits to determine the traffic safety of the railway bridge. © 2016 Computer-Aided Civil and Infrastructure Engineering},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Yenkanchi2016983,
author={Yenkanchi, S. and Wu, Q.M.J.},
title={Cooperative fusion for road obstacles detection using laser scanner and camera},
journal={Proceedings of the World Congress on Intelligent Control and Automation (WCICA)},
year={2016},
volume={2016-September},
pages={983-986},
doi={10.1109/WCICA.2016.7578528},
art_number={7578528},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991571373&doi=10.1109%2fWCICA.2016.7578528&partnerID=40&md5=5974b79035a2941c91414d3528ab8a1a},
affiliation={Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada},
abstract={In order to account for robustness of Automotive safety applications, fusion of data from multiple sensors is of remarkable importance to know the position of road obstacles. Challenges arise in Multi Sensor Data Fusion (MSDF) due to sensor uncertainty, multiple occluding targets and clutter by changing weather conditions. The proposed architecture address the problem by fusing information cooperatively from Laser scanner and monocular camera for robust detection of scene objects in the vehicle environment. The Fusion steps in the proposed method involve the application of the M-estimator SAmple Consensus (MSAC) algorithm for ground plane removal and density based clustering of laser data. Then the filtered laser objects are projected on the image plane and the corresponding region of interest (ROI) is extracted to localize the potential targets. Experimental results on challenging scene sequences of benchmark data sets prove the robustness of proposed fusion architecture for detecting vehicles on the road. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ran2016451,
author={Ran, C. and Dou, Y.},
title={Information fusion full-order Kalman filter for multisensor descriptor system},
journal={Proceedings of the World Congress on Intelligent Control and Automation (WCICA)},
year={2016},
volume={2016-September},
pages={451-456},
doi={10.1109/WCICA.2016.7578318},
art_number={7578318},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991688470&doi=10.1109%2fWCICA.2016.7578318&partnerID=40&md5=5254ab40862b7dfc8feb29cd89a73224},
affiliation={Department of Automation of Heilongjiang University, Harbin, China},
abstract={For the multisensor linear stochastic descriptor system, the information fusion full-order descriptor Kalman filters are presented, which are different from the reduced-order Kalman filtering algorithms and can improve filtering accuracy. The centralized fusion full-order descriptor Kalman filter can obtain the globally optimal filter, by extending all measurement information. The weighted measurement fusion full-order descriptor Kalman filter also has global optimality and has less computational burden, based on the least square method. The state fusion and sequential covariance intersection full-order descriptor Kalman filters yield the suboptimal filters. The sequential covariance intersection full-order descriptor Kalman filter avoids computing the cross-variance of local full-order Kalman filters and can reduce the computational burden. The accuracy of the centralized fusion and the weighted measurement fusion full-order Kalman filter is higher than that of state fusion and the sequential covariance intersection fusion full-order Kalman filter, and the accuracy of these algorithms are all higher than that of local full-order Kalman filters. A simulation example verifies the effectiveness. © 2016 IEEE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Jin2016992,
author={Jin, X.a  b  and Yin, G.a  and Chen, N.a  and Chen, J.a  and Zhang, N.a },
title={State observation of distributed drive electric vehicle using square root cubature Kalman filter},
journal={Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition)},
year={2016},
volume={46},
number={5},
pages={992-996},
doi={10.3969/j.issn.1001-0505.2016.05.016},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991038645&doi=10.3969%2fj.issn.1001-0505.2016.05.016&partnerID=40&md5=983bb644ef0e987b8c28ca31fc340cfb},
affiliation={School of Mechanical Engineering, Southeast University, Nanjing, China; Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, United States},
abstract={To deal with nonlinear challenges on vehicle dynamics state estimation, the eight-DOF(degree of freedom) nonlinear vehicle dynamics state estimation system, including longitudinal, lateral, yaw, and roll motions was constructed by introducing a nonlinear dynamics Dugoff tire model. Based on multi-sensor data fusion, the nonlinear observer with square root cubature Kalman filter was designed to estimate some key parameters, such as lateral tire-road forces and vehicle sideslip angle. Then the co-simulation platform with Simulink-Carsim for the estimated system of distributed drive electric vehicles was built in Matlab/Simulink environment. Simulations for double lane change manoeuvre were carried out to evaluate the feasibility and the effectiveness of the observer. The results show that the observed values with traditional extended Kalman filter state observer deviate from the real values of the vehicle running state when vehicles deliver high lateral acceleration, while the nonlinear observer with the proposed square root cubature Kalman filter has smooth results and reflects the real-time nonlinear vehicle dynamics state during double lane change manoeuvre. And it possesses smaller observer errors and higher observation precision. © 2016, Editorial Department of Journal of Southeast University. All right reserved.},
author_keywords={Electric vehicles;  Square root cubature Kalman filter;  State observation;  Vehicle dynamics},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Shivashankarappa2016375,
author={Shivashankarappa, N. and Adiga, S. and Avinash, R.A. and Janardhan, H.R.},
title={Kalman filter based multiple sensor data fusion in systems with time delayed state},
journal={3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016},
year={2016},
pages={375-382},
doi={10.1109/SPIN.2016.7566723},
art_number={7566723},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991730076&doi=10.1109%2fSPIN.2016.7566723&partnerID=40&md5=08e9a9c3df85a56640c5223c75acb6e0},
affiliation={Dept of Telecommunication Engineering, M S Ramaiah Institute of Technology, Bangalore, India},
abstract={Obtaining accurate data in any system is a challenging problem. Multi-sensor data fusion is a widely used technique to improve the accuracy. In this paper, measurement level fusion, covariance union fusion, and state vector fusion based on Kalman filters for systems with delayed states is presented. The obtained Kalman filter based data fusion algorithms for time delayed systems are then modified to handle the missing measurements which is a common problem in wireless sensor networks. The simulation results of the data fusion algorithms obtained using MATLAB are compared. The performances of the fusion filters are found to be similar and the choice of a fusion algorithm would only depend on the particular application. © 2016 IEEE.},
author_keywords={covariance union fusion;  Kalman filter;  measurement level fusion;  missing measurements;  multisensor data fusion;  state vector fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yuqing2016748,
author={Yuqing, L.a  and Tianshe, Y.b  and Jian, L.c  and Na, F.b  and Guan, W.b },
title={A fault diagnosis method by multi sensor fusion for spacecraft control system sensors},
journal={2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016},
year={2016},
pages={748-753},
doi={10.1109/ICMA.2016.7558656},
art_number={7558656},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991270668&doi=10.1109%2fICMA.2016.7558656&partnerID=40&md5=4112fa0bf65e9971afd8623fc22b3d68},
affiliation={Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin, China; Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit, China; Satellite System Status Analysis Section, Xi'An Satellite Control Center of China xi'An, China},
abstract={Aiming at the problem that the sensor fault in spacecraft control system is difficult to detect and locate, this paper proposes a fault diagnosis algorithm based on multi-sensor fusion method. Firstly, the proposed method establishes the mathematical model of star sensor, earth sensor, and sun sensor. Secondly, this paper analyzes the relationship and redundant information between various sensors. Thirdly, using the principle that the inner product of two vectors should be same in different coordinate system, this paper extracts the detection characteristics and fault diagnosis rules. Through the analysis of simulation data, the correctness and effectiveness of the proposed method is verified. © 2016 IEEE.},
author_keywords={fault diagnosis;  Multi sensor data fusion;  sensor;  spacecraft},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou201620,
author={Zhou, J.a  b  and Ma, C.-H.a  b  and Liu, L.-F.a  b  and Sun, L.-J.a  b  and Xiao, F.a  b },
title={Multi-sensor data fusion method for water quality evaluation based on interval evidence theory},
journal={Tongxin Xuebao/Journal on Communications},
year={2016},
volume={37},
number={9},
pages={20-29},
doi={10.11959/j.issn.1000-436x.2016174},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992047664&doi=10.11959%2fj.issn.1000-436x.2016174&partnerID=40&md5=86ffe66b264ce3a4fe190edb28d5214e},
affiliation={College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China},
abstract={For the inevitable uncertainty and random uncertainty in the process of measuring water quality data with the sensor network, a multi-sensor data fusion method for water quality evaluation based on interval evidence theory was proposed. Considering the precision error of sensor and the abnormalities of measured data, every water quality data measured by sensor was represented by interval number. By calculating the distance between the water quality data and the features of each water quality class, the interval evidence of water quality class was acquired. According to the interval evidence combining rule, a comprehensive interval evidence was obtained by combining the interval evidence of each sensor. Finally, the water quality class was determined based on the comprehensive interval evidence by the decision rule. Experiments show that the proposed method can evaluate water quality class more accurately from the uncertain water quality data. © 2016, Editorial Board of Journal on Communications. All right reserved.},
author_keywords={Interval evidence theory;  Multi-sensor data fusion;  Sensor network;  Uncertainty;  Water quality evaluation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ando20161960,
author={Ando, B. and Baglio, S. and Lombardo, C.O. and Marletta, V.},
title={A multisensor data-fusion approach for ADL and fall classification},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2016},
volume={65},
number={9},
pages={1960-1967},
doi={10.1109/TIM.2016.2552678},
art_number={7470501},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969581115&doi=10.1109%2fTIM.2016.2552678&partnerID=40&md5=48834e340ca745a1c4f9ee8adaf72b3c},
affiliation={Department of Electric, Electronic and Information Engineering, University of Catania, Catania, Italy},
abstract={This paper deals with an advanced approach for the monitoring of elders and people with neurological pathologies (e.g., Alzheimer). The system that adopts a multisensor approach is able to recognize critical events, such as falls or prolonged inactivity, to monitor the user posture, and to notify the alerts to caregivers. In particular, this paper focuses on smart algorithms developed for the activities of daily living (ADL) classification, which use the information provided by inertial sensors embedded in the user device. In particular, a novel multisensor data-fusion approach, combining data from an accelerometer and a gyroscope, is presented. Apart from alert management, the information provided by this system is useful to track the evolution of the user pathology, also during rehabilitation tasks. Results obtained during tests with users demonstrate suitable performances of the adopted paradigm, in terms of sensitivity and specificity, in performing falls and ADL classification tasks. The average value of the sensitivity index for the classes of falls and ADL considered through this paper is 0.81, while the average value of the specificity index is 0.98. © 1963-2012 IEEE.},
author_keywords={activities of daily living (ADL);  Ambient Assisted Living (AAL);  fall detection;  inertial sensors;  smartphone;  threshold algorithm;  user posture;  user safety monitoring},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hu201665,
author={Hu, J.a  and Wang, Z.b  c  and Chen, D.a  and Alsaadi, F.E.c },
title={Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects},
journal={Information Fusion},
year={2016},
volume={31},
pages={65-75},
doi={10.1016/j.inffus.2016.01.001},
note={cited By 23},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955481474&doi=10.1016%2fj.inffus.2016.01.001&partnerID=40&md5=7eb1fb7e67780404f326c9256a99e433},
affiliation={Department of Applied Mathematics, Harbin University of Science and Technology, Harbin, China; Department of Computer Science, Brunel University London, Uxbridge, Middlesex, United Kingdom; Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia},
abstract={In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multi-objective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted. © 2016 Elsevier B.V. All rights reserved.},
author_keywords={Estimation;  Filtering;  Multi-sensor data fusion;  Network-induced phenomena;  Networked systems},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Wang2016794,
author={Wang, F. and Tian, D. and Wang, Y.},
title={High accuracy inertial stabilization via Kalman filter based disturbance observer},
journal={2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016},
year={2016},
pages={794-802},
doi={10.1109/ICMA.2016.7558664},
art_number={7558664},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991281651&doi=10.1109%2fICMA.2016.7558664&partnerID=40&md5=20582cceaa1cd4a05167dcf5216f90ea},
affiliation={Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, China},
abstract={Stabilized control is one of the key technologies of the aerial photoelectric stabilization system. It is difficult for classical control method to further improve the inertial stabilization performance of the aerial photoelectric platform. Disturbance observer(DOB) is effective to estimate external disturbance and system uncertainties such as external friction torque, windage, inertia variation, to realize the compensation of such factors. However, in a practical application, as one of the key parts of the stabilized control system, the dynamic performance of measurement device directly impacts on the dynamic characteristic and reliability of the DOB. In this paper, firstly we analyse the impact on its dynamic performance if measurement sensors exist phase lag. Next, accoding to the analyzed results, putting forward a high accuracy inertial stabilization method combining Kalman filter and disturbance observer, and the algorithm can effectively improve the dynamic performance of control system. © 2016 IEEE.},
author_keywords={Disturbance Observer;  Kalman-filter;  multisensor fusion;  Photoelectric Stabilized Platform},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tootooni2016,
author={Tootooni, M.S.a  and Rao, P.K.b  and Chou, C.-A.c  and Kong, Z.d },
title={A spectral graph theoretic approach for monitoring multivariate time series data from complex dynamical processes},
journal={IEEE Transactions on Automation Science and Engineering},
year={2016},
volume={PP},
number={99},
doi={10.1109/TASE.2016.2598094},
art_number={7556960},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986237219&doi=10.1109%2fTASE.2016.2598094&partnerID=40&md5=76d230f42d644c6aeb3989cfe930945d},
affiliation={Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, United States; Department of Mechanical and Materials Engineering, University of Nebraska, Lincoln, NE, United States; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States; Grado Department of Industrial and System Engineering, Virginia Tech, Blacksburg, VA, United States},
abstract={The objective of this paper is to monitor complex process dynamics manifest in multivariate (multidimensional) time series data using a spectral (algebraic) graph theoretic approach. We test the hypothesis that the spectral graph-based topological invariants detect incipient process drifts earlier [lower average run length (ARL)] and with higher fidelity (consistency of detection) when compared with the conventional statistics-based approaches. The presented approach maps a multidimensional sensor data stream XNxd (visualize N as time and d as the number of sensors) as an unweighted and undirected network graph G(V,E), indexed by its vertices V and edges E, i.e., X |→ G(V,E). The rationale is that the graph-based topological invariants are surrogate representatives of the system state. We compare the monitoring performance of spectral graph theoretic invariants with conventional statistical features in an exponentially weighted moving average control chart setting. The practical utility of the approach is substantiated in the context of process monitoring in two advanced manufacturing scenarios, namely, ultraprecision machining (UPM) and semiconductor chemical mechanical planarization. These studies corroborate the hypothesis that graph theoretic invariants, when used as monitoring statistics, lead to lower ARL and more consistent detections in contrast to conventional statistical features. For instance, in the UPM case, the fault detection delay using graph theoretic invariants is less than 160 ms, compared with over 8 s of delay with statistical features. © 2004-2012 IEEE.},
author_keywords={Anomaly detection;  chemical mechanical planarization (CMP);  data fusion;  Fiedler number;  multisensor monitoring;  sensor fusion;  spectral graph theory;  ultraprecision machining (UPM).},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Salentinig201667,
author={Salentinig, A. and Gamba, P.},
title={Multiscale multisensor decision level data fusion for urban mapping},
journal={4th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2016 - Proceedings},
year={2016},
pages={67-71},
doi={10.1109/EORSA.2016.7552768},
art_number={7552768},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987968712&doi=10.1109%2fEORSA.2016.7552768&partnerID=40&md5=7f6f8b5ab998361ecdcf07ef73c32cab},
affiliation={Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy},
abstract={The combination of multiple maps of urban area extents is becoming more and more common, as the availability of multiple EO data sets is increasing constantly, and different teams develop efficient techniques to extract human settlements from SAR and optical data. The next challenge is the need to combine all of these maps using an approach suitable to multi-source but also multi-resolution data sets. This work provides a first step towards this aim, by considering a 'pixel aggregation' technique which proves to be more effective than standard techniques to resample all data sets on a common grid at the intended spatial resolution. Results on Beijing using SAR data from three different sensors and multispectral data from Landsat are offered to prove the effectiveness of the proposed procedure. © 2016 IEEE.},
author_keywords={multiscale data fusion;  multispectral data;  SAR;  urban mapping},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lin2016,
author={Lin, B.-Z. and Lin, C.-C.},
title={Pedestrian detection by fusing 3D points and color images},
journal={2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings},
year={2016},
doi={10.1109/ICIS.2016.7550787},
art_number={7550787},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987948003&doi=10.1109%2fICIS.2016.7550787&partnerID=40&md5=6f6ea919ecfc8d43aeadeb0382d792b9},
affiliation={Dept. of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan},
abstract={In this paper, a fusing approach of a 3D sensor and a camera are used to improve the reliability of pedestrian detection. The proposed pedestrian detecting system adopts DBSCAN to cluster 3D points and projects the candidate clusters onto images as region of interest (ROI). Those ROIs are detected by HOG (histograms of oriented gradients) pedestrian detector. Because the DBSCAN groups together 3D points and rejects outlier points correctly, the proposed system has a low false detection rate. The performance is also improved since the proposed system only detects the ROI instead of the whole color image. © 2016 IEEE.},
author_keywords={data fusion;  histograms of oriented gradients (HOG);  LIDAR;  multisensor;  pedestrian detection},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zan20161075,
author={Zan, H.a  and He, K.a  and Zhu, Y.a  and Wang, M.a  and Dai, F.b },
title={Mine gallery rig performance test based on BP neural network and multi-sensor data fusion},
journal={Proceedings - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016},
year={2016},
pages={1075-1079},
doi={10.1109/IS3C.2016.271},
art_number={7545381},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986190058&doi=10.1109%2fIS3C.2016.271&partnerID=40&md5=09fdf5ce6a764fa595b87b5a708b2296},
affiliation={School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, China; Quality Inspection Center, Xi'an Research Institute of CCTEG, Xi'an, China},
abstract={Traditional gallery rig's factory inspection process is complex and the result is not accurate. It is necessary to find a simple way to realize the factory inspection. In this paper multisensor data fusion based on Grubbs criterion is used in gallery rig performance test, and a classification algorithm based on BP neural network is analyzed and simulated. The results showed the validity of the algorithm, and it successfully realized the accurate evaluation of the gallery rig performance. So it has a strong practical value under the premise of both control costs and ensure the evaluation effect. © 2016 IEEE.},
author_keywords={BP neural network;  Data fusion;  Gallery rig;  Grubbs criterion;  Multi-sensor},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lohan2016,
author={Lohan, E.S.a  and Talvitie, J.a  and Granados, G.-S.b },
title={Data fusion approaches for WiFi fingerprinting},
journal={Proceedings of 2016 International Conference on Localization and GNSS, ICL-GNSS 2016},
year={2016},
doi={10.1109/ICL-GNSS.2016.7533847},
art_number={7533847},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986197506&doi=10.1109%2fICL-GNSS.2016.7533847&partnerID=40&md5=fc389bcc9855d01fa19ecdfcbc4b8527},
affiliation={Tampere University of Technology, Tampere, Finland; Universitat Autonoma de Barcelona, Spain},
abstract={WiFi localization problem is basically a multi-sensor data fusion. This paper investigates the use of Bayesian and non-Bayesian Dempster Shafer (DS) data fusion in the context of WiFi-based indoor positioning via fingerprinting. Two novel DS mass choices are discussed. The positioning results are based on real-field measurement data from nine distinct multi-floor buildings in two countries. It is shown that a proper mass choice is crucial in DS processing and that, in spite of taking into account the data uncertainty, the DS data fusion is not offering significant advantage in terms of positioning performance over the Bayesian data fusion. © 2016 IEEE.},
author_keywords={3D localization;  Bayesian data fusion;  Dempster Shafer (DS) theory;  Received Signal Strength (RSS)},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Chunshan20163882,
author={Chunshan, Y.a  b  and Zhibo, Y.a  and Zili, D.a },
title={Distributed fusion guaranteed cost robust Kalman filter with uncertain noise variances},
journal={Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016},
year={2016},
pages={3882-3888},
doi={10.1109/CCDC.2016.7531663},
art_number={7531663},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983792057&doi=10.1109%2fCCDC.2016.7531663&partnerID=40&md5=d92f70243c54224614fd4b3e268a02d7},
affiliation={Department of Automation, Heilongjiang University, Harbin, China; Heilongjiang College of Business and Technology, Harbin, China},
abstract={Two classes of guaranteed cost robust weighted fusion Kalman filters are presented for multisensor system with uncertain noise variances based on minimax robust estimation principle and parameterization representation of perturbations of uncertain noise variances. 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 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. © 2016 IEEE.},
author_keywords={guaranteed cost robustness;  Lyapunov equation approach;  minimax Kalman filter;  multisensor information fusion;  uncertain noise variance;  weighted fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Aquino201659,
author={Aquino, G. and Pirmez, L. and De Farias, C.M. and Delicato, F.C. and Pires, P.F.},
title={Hephaestus: A multisensor data fusion algorithm for multiple applications on wireless sensor networks},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={59-66},
art_number={7527870},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992108900&partnerID=40&md5=51e2c834e5f4f75ebff07d7737c1d59b},
affiliation={PPGI- INCE/DCC-IM, Universidade Federal Do Rio de Janeiro, Rio de Janeiro, Brazil},
abstract={Wireless sensor networks (WSN) are core components of the Internet of Things paradigm. By applying techniques such as sensor virtualization, the wireless sensor network infrastructure can be shared by a set of applications. On such scenario, the massive amount of data produced by the widely spread sensors produces a value-added information for the end user. By sharing the same infrastructure with multiple users, the set of applications in execution may change quickly, according to the users' necessity. In this case, the application requirements (for instance, data intervals or the potential events of interest) may not be known a priori. In this work, we aim to provide a solution to properly integrate data from multiple applications without the knowledge about specific application requirements, and unconver useful information from such data. We propose Hephaestus, an entropic information fusion algorithm, which uses Mean, Kurtosis and Skewness to apply a heuristics that divides the dataset into multiple features, for multiple applications. Hephaestus achieved high accuracy while incurring in low overhead for the resource constrained devices of WSNs. © 2016 ISIF.},
author_keywords={Data association;  Entropy;  estimation;  wireless sensor networks},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou20161354,
author={Zhou, J. and Cai, S.-Q. and Zhu, W.-N.},
title={Multi-criteria decision model of multi-sensor data fusion},
journal={Kongzhi yu Juece/Control and Decision},
year={2016},
volume={31},
number={8},
pages={1354-1362},
doi={10.13195/j.kzyjc.2015.0940},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982943742&doi=10.13195%2fj.kzyjc.2015.0940&partnerID=40&md5=770c65468cf87d3ebd190201918a647a},
affiliation={College of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing, China},
abstract={A new multi-sensor data fusion method using multi-criteria decision aid is proposed, which considers several indicators to reduce the decision subjectivity and contingency. Different fusion levels in the multi-sensor target recognition system is proposed to define multiple criteria, hence to gain more information of multi-criteria qualitatively. With no information about the preference of the decision maker for each criteria, the criteria weights vector can be obtained by solving a multi-objective optimization which intends to minimize the criteria redundancy and maximize the diversity factor of every criteria. The simulation results show that the proposed method performs better in anti-risk capability and stability than the mono-criteria and decision-making level method. © 2016, Editorial Office of Control and Decision. All right reserved.},
author_keywords={Multi-criteria;  Multi-sensor data fusion;  Target recognition;  Vague set},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li2016843,
author={Li, S.a  and Hedley, M.a  and Collings, I.B.b  and Johnson, M.c },
title={Accurate tracking in NLOS environments using integrated IMU and fixed lag smoother},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={843-848},
art_number={7527974},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992076990&partnerID=40&md5=52dbc8b806ce4328562684f929585fea},
affiliation={Commonwealth Scientific and Industrial, Research Organisation (CSIRO), Marsfield, NSW, Australia; Macquarie University, North Ryde, NSW, Australia; CSIRO, Marsfield, NSW, Australia},
abstract={Multi-sensor data fusion using Inertial Measurement Units (IMUs) is a promising technique for improving the performance of positioning systems. However, the performance of conventional sensor fusion algorithms based on the Kalman Filter (KF) is compromised in indoor environments due to non-line-of-sight (NLOS) propagation. In this paper, we propose a semi-real time tracking algorithm which uses a fixed lag smoother for sensor fusion and achieves high accuracy in NLOS environments. The computational complexity of the algorithm is taken into consideration and is reduced by decreasing the operating rate of the smoother. The performance of the proposed algorithm is validated experimentally using a real indoor positioning platform. It is shown that the 90th percentile positioning error for a pedestrian is reduced by 42% using the proposed semi-real time tracking algorithm with 10 s lag, compared with using a KF-based real time tracking algorithm. © 2016 ISIF.},
author_keywords={IMU;  indoor localization;  NLOS error;  sensor fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Motwani2016491,
author={Motwani, A.a  and Liu, W.b  and Sharma, S.a  and Sutton, R.a  and Bucknall, R.b },
title={An interval Kalman filter-based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle},
journal={Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment},
year={2016},
volume={230},
number={3},
pages={491-507},
doi={10.1177/1475090215596180},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978481191&doi=10.1177%2f1475090215596180&partnerID=40&md5=40c89eaebe043ac0666dfc3ca4cce6e8},
affiliation={Marine and Industrial Dynamic Analysis (MIDAS), Research Group, School of Marine Science and Engineering, Plymouth University, Drake Circus, Plymouth, Devon, United Kingdom; Marine Research Group, Department of Mechanical Engineering, University College London, London, United Kingdom},
abstract={This article presents a novel fuzzy-logic based multi-sensor data fusion algorithm for combining heading estimates from three separate weighted interval Kalman filters to construct a robust, fault-tolerant heading estimator for the navigation of the Springer autonomous surface vehicle. A single, low-cost gyroscopic unit and three independent compasses are used to acquire data onboard the vehicle. The gyroscope data, prone to sporadic bias drifts, are fused individually with readings from each of the compasses via a weighted interval Kalman filter. Unlike the standard Kalman filter, the weighted interval Kalman filter is able to provide a robust heading estimate even when subject to such gyroscope bias drifts. The three ensuing weighted interval Kalman filter estimates of the vehicle's heading are then fused via a fuzzy logic algorithm designed to provide an accurate heading estimate even when two of the three compasses develop a fault at any time. Simulations and real-time trials demonstrate the effectiveness of the proposed method. © IMechE 2015.},
author_keywords={autonomous surface vehicle;  fuzzy logic;  interval Kalman filtering;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Kubertschak2016347,
author={Kubertschak, T.a  and Maehlisch, M.a  and Wuensche, H.-J.b },
title={Fusion routine independent implementation of advanced driver assistance systems with polygonal environment models},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={347-354},
art_number={7527909},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992084557&partnerID=40&md5=0988dd7dbb0507e579cbed0ce08f636f},
affiliation={AUDI AG, Ingolstadt, Germany; Universität der Bundeswehr München, Technik Autnonomer Systeme (TAS), Neubiberg, Germany},
abstract={Future driver assistance systems will rely heavily on fusion systems and an exact knowledge of the static environment. Several aspects like driving path detection, parking assistants, emergency braking and trajectory planning need the knowledge about non-moving objects in the surroundings of the vehicle. In order to build robust maps of the environment, several possibly conflicting sensor readings must be combined. However, an increased number of sensors and an increased number of driver assistance functions lead to difficulties on integration in the fusion system. Different interfaces must be supported, since sensors provide their data in special formats and functions require each other aspects of the environment. This difficulty has been relaxed by introducing the Fences-architecture and homogeneous interfaces. The architecture leads to several useful properties. This contribution deals with a particular property, the independence of the fusion subsystem from all other subsystems. This property states, that driver assistance functions are executable without modifications, in case the mapping technique is changed or even removed. The property is demonstrated by implementing two different mapping strategies. The environment models created by these strategies as well as an environment model created by a laser scanner are afterwards used to execute a road boundary extractor, which is readily introduced. © 2016 ISIF.},
author_keywords={Advanced Driving Assistance Systems;  Multi-sensor Data Fusion;  Static Environment;  Unified Architecture},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kumar201618,
author={Kumar, S. and Hegde, R.M.},
title={Multi-sensor data fusion methods for indoor localization under collinear ambiguity},
journal={Pervasive and Mobile Computing},
year={2016},
volume={30},
pages={18-31},
doi={10.1016/j.pmcj.2015.09.001},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950973036&doi=10.1016%2fj.pmcj.2015.09.001&partnerID=40&md5=de8ec365b4cd7f66954aea477591c932},
affiliation={Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India},
abstract={Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer-Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Collinearity;  Least square;  Multi-sensor data fusion;  Node localization;  Singular value decomposition},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Battistello20161881,
author={Battistello, G.a  and Gonzalez, J.a  and Ulmke, M.a  and Koch, W.a  and Mohrdieck, C.b },
title={Multi-sensor maritime monitoring for the Canadian Arctic: Case studies},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={1881-1888},
art_number={7528112},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992117690&partnerID=40&md5=99ebe0f6739294d0db0b2763eae8ad05},
affiliation={Department Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany; Data Fusion Concepts, Integration and Test, Airbus Defence and Space, Ulm, Germany},
abstract={Maritime Domain Awareness (MDA) in the Canadian Arctic is limited by the scarceness or even absence of monitoring sensors. The backbone for vessel surveillance is represented by the satellite-based Automatic Identification System (S-AIS), which provides important though not comprehensive information about ships in the area. The objective of the Canadian-German research project PASSAGES is to specify the requirements and the modular architecture of a persistent maritime surveillance and risk management system adapted to arctic conditions and focused on the Northwest Passage (NWP). In order to provide a continuous vessel traffic picture in remote and local areas, real and simulated sensors from space borne and ground based platforms besides S-AIS are taken into account. The capabilities of such multi-sensor surveillance solutions in enhancing the MDA are demonstrated over two selected operationally relevant scenarios. A key element to achieve this, are robust multisource tracking and fusion algorithms able to cope with different heterogeneous and potentially delayed, imprecise or misaligned sensor data. © 2016 ISIF.},
author_keywords={Maritime Domain Awareness;  Multi-Sensor Fusion;  Passive Radar;  Traffic Monitoring},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{VonMarcard20161533,
author={Von Marcard, T.a  and Pons-Moll, G.b  and Rosenhahn, B.a },
title={Human Pose Estimation from Video and IMUs},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2016},
volume={38},
number={8},
pages={1533-1547},
doi={10.1109/TPAMI.2016.2522398},
art_number={7393844},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978427623&doi=10.1109%2fTPAMI.2016.2522398&partnerID=40&md5=eb4fb2e0d8925dcd940350783d9aa794},
affiliation={TNT Group, Leibniz-University of Hannover, Germany; Perceiving Systems Department of the Max Planck for Intelligent Systems, Tuebingen, Germany},
abstract={In this work, we present an approach to fuse video with sparse orientation data obtained from inertial sensors to improve and stabilize full-body human motion capture. Even though video data is a strong cue for motion analysis, tracking artifacts occur frequently due to ambiguities in the images, rapid motions, occlusions or noise. As a complementary data source, inertial sensors allow for accurate estimation of limb orientations even under fast motions. However, accurate position information cannot be obtained in continuous operation. Therefore, we propose a hybrid tracker that combines video with a small number of inertial units to compensate for the drawbacks of each sensor type: on the one hand, we obtain drift-free and accurate position information from video data and, on the other hand, we obtain accurate limb orientations and good performance under fast motions from inertial sensors. In several experiments we demonstrate the increased performance and stability of our human motion tracker. © 1979-2012 IEEE.},
author_keywords={animation;  Human pose estimation;  IMU;  inertial sensors;  motion capture;  multisensor fusion},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Meyer2016719,
author={Meyer, F.a  and Braca, P.a  and Willett, P.b  and Hlawatsch, F.c },
title={Tracking an unknown number of targets using multiple sensors: A belief propagation method},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={719-726},
art_number={7527958},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992163563&partnerID=40&md5=c2218c7bab20cfed4441e1269074b5b3},
affiliation={Centre for Maritime Research and Experimentation, La Spezia, Italy; Department of ECE, University of Connecticut, Storrs, United States; Institute of Telecommunications, TU Wien, Vienna, Austria},
abstract={We propose a multisensor method for tracking an unknown number of targets. Low computational complexity and very good scalability in the number of targets, number of sensors, and number of measurements per sensor are achieved by running a belief propagation (BP) message passing scheme on a suitably devised factor graph. Using a redundant formulation of data association uncertainty and augmented target states including target indicators allows the proposed BP method to leverage statistical independencies for a drastic reduction of complexity. The proposed method is shown to outperform previously proposed multisensor methods for multitarget tracking, including methods with a less favorable scaling behavior. © 2016 ISIF.},
author_keywords={belief propagation;  data association;  factor graph;  message passing;  Multitarget tracking;  sensor network},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Shapero2016409,
author={Shapero, S.A.a  and Hughes, H.b  and Tuuk, P.b },
title={Adaptive semi-greedy search for multidimensional track assignment},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={409-415},
art_number={7527917},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992028010&partnerID=40&md5=915d7afe394de08a5927e3accae35584},
affiliation={Electro-Optical Systems Laboratory, Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, United States; Sensors and Electromagnetic Applications Laboratory, Georgia Tech Research Institute, Georgia Institute of Technology, Smyrna, United States},
abstract={Robust multitarget, multisensor fusion algorithms rely on optimal or near-optimal assignment algorithms at their core. Greedy and semi-greedy search algorithms represent a fast and effective solution to multi-dimensional assignment problems, at the cost of often producing sub-optimal results in challenging cases. The Adaptive Semi-Greedy Search (ASGS) presented here is an improvement on older semi-greedy search methods, and is more likely to find the optimal solution without sacrificing computational speed. ASGS outperforms Semi Greedy Track Selection (SGTS) in a wide range of statistical tests, where it was consistently more likely to find the optimal solution to 6 dimensional assignment problems, as if the RMS measurement error had been reduced by 25%. The two algorithms are also compared in several challenging multitarget, multisensor tracking scenarios, including maneuvering formations and crossing targets, where a Multiple Hypothesis Tracker (MHT) using the ASGS significantly reduces the RMS position error of the tracks (p < 0.001 for each scenario). The ASGS effectively increases the range of scenarios in which semi-greedy assignment algorithms can be reliably employed, especially valuable for real time applications with large number of targets and sensors. © 2016 ISIF.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Woodstock20161695,
author={Woodstock, T.-K. and Radke, R.J. and Sanderson, A.C.},
title={Sensor fusion for occupancy detection and activity recognition using time-of-flight sensors},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={1695-1701},
art_number={7528087},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992029056&partnerID=40&md5=2e22aed154928a5bd4da68ddebb4cbe5},
affiliation={Center for Lighting Enabled Systems and Applications (LESA), Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States},
abstract={New technologies in lighting enable the design of illumination systems that autonomously meet the needs of occupants. In recent years, lighting design for both commercial and residential spaces has advanced beyond task performance, to a broader range of occupant needs including economic, energy and environmental constraints, architectural integration, human health, productivity, interpersonal communication, and aesthetic quality. Such systems require distributed sensing and control systems with sensory feedback to detect the lighting conditions in the space. Occupancy sensing and activity recognition are core components of this distributed sensor-based control system, and multisensor fusion of integrated sensors is viewed as a key attribute. This paper describes an expanded view of multisensor technologies, signal processing and pattern recognition algorithms that are being developed and evaluated for occupancy detection and activity recognition. This paper presents a multisensor testbed system that incorporates a sparse array of time-of-flight range sensors with pattern recognition algorithms for geometric form and motion detection of human occupants. Three levels of analysis are implemented: (1) Occupant detection and tracking, (2) Occupant pose classification (sitting, standing, and walking), (3) Occupant activity sequence recognition. These algorithms are dependent on statistical training of human pose and motion and implemented with a Bayesian formulation for detection, classification, and recognition. Evaluation of resulting performance in the testbed conference room demonstrates pose and activity recognition accuracy of greater than 97% for single occupants. © 2016 ISIF.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Focke2016643,
author={Focke, R.W.a  b  and De Villiers, J.P.a  c  and Inggs, M.R.b },
title={Speeding up IA mechanically-steered multistatic radar scheduling with GP-GPUs},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={643-650},
art_number={7527948},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992166551&partnerID=40&md5=62c3d62c016e42224a9f724dd94e3ae9},
affiliation={CSIR, Pretoria, South Africa, South Africa; University of Cape Town, Cape Town, South Africa; University of Pretoria, Pretoria, South Africa},
abstract={In this paper, we investigate speeding up the execution time of Interval Algebra (IA) mechanically-steered multistatic and multisite radar scheduling using a general-purpose graphical processing unit (GP-GPU). Multistatic/multisite radar scheduling forms part of JDL fusion level 4, Process Refinement, and specifically draws from the multisensor management domain of knowledge. Pseudo code for an Open Compute Language (OpenCL) IA total-path consistency algorithm is provided based on the original work of Ladkin and Maddux. Monte-Carlo executions are run to solve randomly generated Interval Algebra networks on a GP-GPU and a single core of a multicore central processing unit. The results indicate that the OpenCL IA total-path consistency algorithm, executed on a GP-GPU in parallel, should be preferred for temporal constraint satisfaction problems where the network is more likely to be consistent. Then for consistent networks this parallel algorithm can provide execution time speed-up between two and three times, within the tested limits, that of the serial algorithm. We present suggestions as to constraints to the OpenCL IA total-path consistency algorithm. © 2016 ISIF.},
author_keywords={GP-GPU;  interval algebra;  multisensor scheduling;  multisite radar;  multistatic radar;  process refinement;  resource management},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kang20162015,
author={Kang, K.a  and Maroulas, V.a  and Schizas, I.D.b  and Blasch, E.c },
title={A multilevel homotopy MCMC sequential Monte Carlo filter for multi-target tracking},
journal={FUSION 2016 - 19th International Conference on Information Fusion, Proceedings},
year={2016},
pages={2015-2021},
art_number={7528130},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992146153&partnerID=40&md5=316190fed2a1b8d26425d84b2a0ed7af},
affiliation={Dept of Mathematics, University of Tennessee, Knoxville, Tennessee, United States; Dept of Electric Engineering, University of Texas, Arlington, Texas, United States; Air Force Research Lab, Rome, New York, United States},
abstract={In this paper, we propose an enhanced sequential Monte Carlo filter through the design of a sophisticated Markov chain Monte Carlo (MCMC) procedure. This MCMC procedure is appended after the resampling step of the generic sequential Monte Carlo filter to efficiently resolve the degeneracy of the algorithm. In this work, we use the effective sample size as a learning criterion to monitor the levels of homotopy in the MCMC procedure at each time instant so that the computational cost is reduced without undermining the accuracy of estimation. We test the algorithm on a multi-target tracking problem and compared to other contemporary approaches such as the bootstrap particle filter and the auxiliary particle filter (APF). Based on two scenarios, the proposed algorithm is administrated to have a significant increase in accuracy. © 2016 ISIF.},
author_keywords={homotopy;  Markov chain Monte Carlo;  multi-target tracking;  Sequential Monte Carlo filters},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Fan2016,
author={Fan, Z.a  and Gao, R.X.b  and Wang, P.b  and Kazmer, D.O.c },
title={Multi-sensor data fusion for improved measurement accuracy in injection molding},
journal={Conference Record - IEEE Instrumentation and Measurement Technology Conference},
year={2016},
volume={2016-July},
doi={10.1109/I2MTC.2016.7520465},
art_number={7520465},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980383787&doi=10.1109%2fI2MTC.2016.7520465&partnerID=40&md5=ba9b68a11deeb4049396005e1758935a},
affiliation={Department of Mechanical Engineering, University of Connecticut Storrs, Storrs, CT, United States; Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States; Department of Plastic Engineering, University of Massachusetts Lowell, Lowell, MA, United States},
abstract={Data fusion enables the integration of multiple sensing data associated with the same physical process for more comprehensive process representation, thereby improving quality control in manufacturing. Based on a correlation analysis of measurement data, the effectiveness of data fusion has been investigated, using precision injection moulding as an application context and the predicted weight of moulded parts as a criterion. The study systematically explains why a multivariate sensor that quantifies four parameters at the same sensor location has consistently outperformed multiple single-parameter commercial sensors under various operation conditions, thereby contributing to the theory of data fusion for measurement enhancement. © 2016 IEEE.},
author_keywords={Accuracy;  Data Fusion;  Measurement},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Shahdoosti2016150,
author={Shahdoosti, H.R. and Ghassemian, H.},
title={Combining the spectral PCA and spatial PCA fusion methods by an optimal filter},
journal={Information Fusion},
year={2016},
volume={27},
pages={150-160},
doi={10.1016/j.inffus.2015.06.006},
art_number={716},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936858181&doi=10.1016%2fj.inffus.2015.06.006&partnerID=40&md5=fa6e3155fab72050b74110a4c411943e},
affiliation={Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran},
abstract={Abstract High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion. The principal component analysis (PCA) method has been a popular choice for the spectral transformation. To propose a new consistent data transformation method in spatial domain, this paper applies the PCA transform to the spatial information of the neighboring pixels. Owing to the fact that the coefficients of PCA are obtained from statistical properties of data, they are adaptive and robust. Then, a new hybrid algorithm is proposed combining the spectral PCA and spatial PCA methods, by an optimal filter to make the synthesized result more similar to what the corresponding multisensors would observe at the high-resolution level. The evaluation of the pan-sharpened images, using global validation indexes, reveals that the proposed approach improves the fusion quality compared with six state of the art fusion methods. © 2015 Elsevier B.V.},
author_keywords={Image fusion;  Optimal filter;  Principal component analysis;  Spatial information;  Spectral information},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Makhlouf2016427,
author={Makhlouf, A.a  b  and Marhic, B.a  and Delahoche, L.a  and Alaoui, L.C.a  and Messaoud, H.b },
title={Building heating management based on the belief functions theory},
journal={2016 5th International Conference on Systems and Control, ICSC 2016},
year={2016},
pages={427-432},
doi={10.1109/ICoSC.2016.7507074},
art_number={7507074},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981188283&doi=10.1109%2fICoSC.2016.7507074&partnerID=40&md5=ec09cf899823c9f4d34c1af5bf93105c},
affiliation={Laboratoire des Technologies Innovantes (LTI) EA 3899, Département Informatique, Avenue des Facultés le Bailly, Amiens Cedex 1, France; Laboratoire de Recherche en Automatique, Traitement du Signal et Image (LARATSI) LR13ES13, ENIM, Rue Ibn El Jazzar, Monastir, Tunisia},
abstract={This paper presents a data fusion system, which models and handles uncertainties in buildings, in order to design a smart heating system. The data fusion model provides a temperature trend that will be inserted in smart and predictive heating systems in order to reduce energy consumption in the building. The multilevel data fusion model is based on the belief functions theory. Different simulations were carried out and give very satisfactory results, in spite of the complexity of the data. © 2016 IEEE.},
author_keywords={Belief Functions Theory;  Building management system;  Multilevel fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Chandrasekaran2016,
author={Chandrasekaran, B. and Conrad, J.M.},
title={Sensor fusion using a selective sensor framework to achieve decision and task execution},
journal={Conference Proceedings - IEEE SOUTHEASTCON},
year={2016},
volume={2016-July},
doi={10.1109/SECON.2016.7506715},
art_number={7506715},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980000647&doi=10.1109%2fSECON.2016.7506715&partnerID=40&md5=b3f7d85ef96bf318476d20e7a0d0e941},
affiliation={Department of Electrical and Computer Engineering, University of North Carolina, Charlotte, United States},
abstract={In this paper, the role of multi-sensor data fusion to achieve better interaction levels for Human Robot Collaboration is highlighted. The decision framework uses a sensor-map that is akin to our brain-map and takes into account the pre-processed information streamed by the sensors and groups the sensors and sensor data before applying to the fusion algorithm to derive a confidence level. The task-map is a dynamic structure that holds relevant details between the tasks and the corresponding sensors involved to execute the task. The confidence generated is again compared against real-world constraints before performing the interaction. In the event of uncertainty, the robot can query the user for assistance before executing any further action, thus demonstrating key interaction with the human. © 2016 IEEE.},
author_keywords={Artificial Intelligence;  brain maps;  hard and soft sensor fusion;  human robot interaction (HRI);  mobile robotics;  multi-sensor data fusion;  sensor based decision},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Tijani2016,
author={Tijani, K.a  and Morea, A.a  and Chiaradia, M.T.a  and Nutricato, R.b  and Guerriero, L.b },
title={Prototype of a multi-platform remote sensing service for fishing forecasting},
journal={EESMS 2016 - 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Proceedings},
year={2016},
doi={10.1109/EESMS.2016.7504846},
art_number={7504846},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980370560&doi=10.1109%2fEESMS.2016.7504846&partnerID=40&md5=b2ebeaf18f805400be61f1d5883ab525},
affiliation={Department of Physics 'M. Merlin', Technical University of Bari, Bari, Italy; Geophysical Applications Processing (GAP) S.r.l., Bari, Italy},
abstract={The present work concerns the development of an automatic Fishing Forecasting System (FiFoS) where satellite observations, ancillary data and in situ measurements (Catch Per Unit Effort) are used to set up, calibrate and validate a fishing forecasting model. Multi-temporal and multi-sensor data fusion techniques are applied to multi-spectral data in order to detect chlorophyll and sea temperature fronts that according to physical models of the upwelling phenomena are related to areas rich of phytoplankton nutrients where a high concentration of pelagic fish is expected. © 2016 IEEE.},
author_keywords={Fishing Forecasting System (FiFoS);  Multi-sensor data fusion;  Ocean Color Analysis;  Potential Fishing Zone (PFZ);  Satellite Remote Sensing},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Makhlouf2016728,
author={Makhlouf, A.a  b  and Marhic, B.a  and Delahoche, L.a  and Alaoui, L.C.a  and Messaoud, H.b },
title={Smart and predictive heating system: Belief model for indoor regulation},
journal={16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2015},
year={2016},
pages={728-733},
doi={10.1109/STA.2015.7505206},
art_number={7505206},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979708969&doi=10.1109%2fSTA.2015.7505206&partnerID=40&md5=e8a3a411c7dcabd07d26a9e124a2cabb},
affiliation={Laboratoire des Technologies Innovantes (LTI), EA 3899, Département Informatique, Avenue des Facultés le Bailly, Amiens Cedex 1, France; Laboratoire de Recherche en Automatique, Traitement du Signal et Image (LARATSI), ENIM, Rue Ibn El Jazzar, Monastir, Tunisia},
abstract={The objective of this paper is to investigate a method to model data uncertainties in order to regulate a smart heating system that reduces energy consumption. To achieve this, we propose a multilevel data fusion system that provides a contextual trend, based on the belief theory of Dempster-Shafer for data combination and the Transferable Belief Model (TBM) to take the decision. The fusion system combines the weather forecast and the thermal comfort associated to the occupant's activities and habits. The challenge we took is complex as the data to be fused are highly uncertain and heterogeneous but our method proved its efficiency as we obtain very satisfactory simulation results. © 2015 IEEE.},
author_keywords={Belief Function Theory;  Building management system;  Modelling uncertainty},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Schöch2016209,
author={Schöch, A.a  b  and Salvadori, A.a  and Carmignato, S.c  and Savio, E.a },
title={Enhancing multisensor data fusion on light sectioning coordinate measuring systems for the in-process inspection of freeform shaped parts},
journal={Precision Engineering},
year={2016},
volume={45},
pages={209-215},
doi={10.1016/j.precisioneng.2016.02.014},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960155829&doi=10.1016%2fj.precisioneng.2016.02.014&partnerID=40&md5=2fad23b1fe52eea88e7f48b2a5e24244},
affiliation={Department of Industrial Engineering, University of Padua, via Venezia 1, Padova, Italy; Institute for Production Metrology, Materials and Optics, NTB Interstate University of Applied Sciences of Technology, Werdenbergstr. 4, Buchs, Switzerland; Department of Management and Engineering, University of Padua, Stradella S. Nicola 3, Vicenza, Italy},
abstract={A method for automatic determination of sensors positions and orientations in multisensor laser triangulation coordinate measuring systems is presented. Based on a series of measurements on a conical artifact without the use of external light sources, the approach allows for both optimized adjustment of the physical sensors alignment and improved 3D data registration by software. Simulations were conducted to quantify the sensitivity of the method. Experimental results based on measurements of steep details of complex shaped parts show a 5-times reduction of deviations between measurements of sensors with respect to an already optimized adjustment by manual methods. © 2016 Elsevier Inc. All rights reserved.},
author_keywords={Laser triangulation;  Manufacturing;  Metrology;  Multisensor data fusion},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Campanile2016480,
author={Campanile, F.a  and Cerullo, G.b  and D'antonio, S.b  and Mazzeo, G.b  and Papale, G.b  and Sgaglione, L.b },
title={A multi-sensor data fusion approach for detecting direct debit frauds},
journal={Proceedings - 2016 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2016},
year={2016},
pages={480-485},
doi={10.1109/INCoS.2016.90},
art_number={7695224},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999028702&doi=10.1109%2fINCoS.2016.90&partnerID=40&md5=579c8d8c49d7467c6c34cee258a881a9},
affiliation={Sync Lab S.r.l., Naples, Italy; University of Naples Parthenope, Naples, Italy},
abstract={Electronic payment systems have always represented an attractive target for cyber criminals. In this context the Single Euro Payments Area Direct Debit (SDD) service is gaining more and more importance since it has been promoted by the European banking industry as an innovative payment infrastructure. This service allows to perform electronic payments across the Euro zone as simple as domestic payments currently are. This schema facilitates the access to new markets by enterprises and reduces the overall cost to move capitals in Europe, but the other side of the coin is that it is the only financial system that has recorded an increase in the number of frauds, as highlighted by European Central Bank in a report dated 2015. In this paper SDD service attack patterns are analysed and a SDD fraud detection system based on multi-sensor data fusion is presented. Specifically, the Dempster-Shafer Theory is used in the proposed system to correlate security-relevant data from multiple information sources in order to detect anomalous behaviours that could be the evidence of an ongoing SDD fraud. © 2016 IEEE.},
author_keywords={Data Fusion;  Direct Debit;  Single Euro Payments Area;  Social Networks},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rand2016,
author={Rand, R.S.a  and Khuon, T.a  and Truslow, E.b },
title={Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion},
journal={Optical Engineering},
year={2016},
volume={55},
number={7},
doi={10.1117/1.OE.55.7.073101},
art_number={073101},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979582078&doi=10.1117%2f1.OE.55.7.073101&partnerID=40&md5=8c1f7ab73642bb6f45796658447cdb11},
affiliation={National Geospatial-Intelligence Agency, 7500 GEOINT Drive, Springfield, VI, United States; Northeastern University, Department of Electrical and Computer Engineering, 360 Huntington Avenue, Boston, MA, United States},
abstract={A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus of University of Southern Mississippi, Gulfport, Mississippi. © The Authors.},
author_keywords={hyperspectral imagery;  LIDAR;  mean shift;  neural networks;  point cloud data;  spectral-spatial fusion;  stochastic expectation-maximization},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ge2016912,
author={Ge, Q.a  and Shao, T.a  and Yang, Q.b  and Shen, X.c  and Wen, C.d },
title={Multisensor Nonlinear Fusion Methods Based on Adaptive Ensemble Fifth-Degree Iterated Cubature Information Filter for Biomechatronics},
journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
year={2016},
volume={46},
number={7},
pages={912-925},
doi={10.1109/TSMC.2016.2523911},
art_number={7445890},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976473548&doi=10.1109%2fTSMC.2016.2523911&partnerID=40&md5=b5dcacafb5bd30c8fd052634f760327a},
affiliation={Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou, China; State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou, China; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China; College of Electrical Engineering, Henan University of Technology, Zhengzhou, China},
abstract={Performance of the Kalman filter (KF) is degraded when dealing with nonlinear dynamic systems. For a kind of nonlinear biomechatronics system, a fifth-degree ensemble iterated cubature square-root information filter (EsFICIF), which can effectively improve estimation performance, is proposed by combing many estimation schemes. Moreover, the associated multisensor fusion is deeply studied based on this proposed nonlinear filter in this paper. That is, four classic nonlinear fusion methods, which include augmented measurements fusion, weighted measurements fusion, sequential filtering fusion, and distributed filtering fusion, are compared on estimation performance. The motivation of this paper is to extend the work on estimation performance comparison of nonlinear fusion methods based on the conventional extended KF and to validate some basic conclusions existed in the traditional linear data fusion theory based on the proposed EsFICIF. The estimation accuracies of the four nonlinear fusion methods are compared and the exchanging property of measurements update order is also discussed. It is observed that, when the measurement properties are identical, the estimation accuracies of augmented measurements fusion, weighted measurements fusion, and distributed feedback fusion are equivalent, while the sequential filtering fusion does not hold. Furthermore, the exchanging property of the measurements update order of the sequential filtering fusion can no longer be guaranteed. These results further show some basic conclusions existed in linear fusion theory are no longer valid for nonlinear systems and the conclusions based on the EKF are still available for more complex nonlinear filters. Finally, numerical examples are provided to validate the results given in this paper. © 2013 IEEE.},
author_keywords={Biomechatronics;  cubature information filter (CIF);  ensemble Kalman filter (EsKF);  fifth-degree;  information fusion;  iterated method;  performance analysis},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shan201656,
author={Shan, Z.a  and Xia, Y.b  and Hou, P.a  and He, J.a },
title={Fusing Incomplete Multisensor Heterogeneous Data to Estimate Urban Traffic},
journal={IEEE Multimedia},
year={2016},
volume={23},
number={3},
pages={56-63},
doi={10.1109/MMUL.2016.37},
art_number={7478438},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982307191&doi=10.1109%2fMMUL.2016.37&partnerID=40&md5=cbc69ef6edd32a21a7d51a14c38b50f7},
affiliation={Hangzhou Normal University, China; Zhejiang University, China},
abstract={Today, data-driven intelligent transportation systems must address data quality challenges, such as the missing data problem. For example, is it possible to improve the performance of traffic state estimation using incomplete data? In this article, an incomplete traffic data fusing method is proposed to estimate traffic state accurately. It improves missing data estimation by extracting data correlations and applying incomplete data fusion, implementing the two approaches in parallel. The main research focus is on extracting the inherent spatio-temporal correlations of traffic states data from road segments based on a multiple linear regression (MLR) model. Computational experiments for accuracy and efficiency demonstrate that this method can use data correlations to implement accurate and real-time traffic state estimation. This article is part of a special issue on quality modeling. © 2016 IEEE.},
author_keywords={correlation extraction;  data analysis;  data fusion;  incomplete data;  intelligent systems;  intelligent transportation systems;  quality modeling;  traffic state estimation},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lin20162874,
author={Lin, Y.a  b  and Wang, C.a  and Ma, C.b  and Dou, Z.a  and Ma, X.c },
title={A new combination method for multisensor conflict information},
journal={Journal of Supercomputing},
year={2016},
volume={72},
number={7},
pages={2874-2890},
doi={10.1007/s11227-016-1681-3},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959359863&doi=10.1007%2fs11227-016-1681-3&partnerID=40&md5=05f8a173e11ace80a8a2e5d71f59d81f},
affiliation={College of Information and Communication Engineering, Harbin Engineering University, No. 145-1, Nantong Street, Nangang District, Harbin, Heilongjiang, China; College of Computer Science and Technology, Harbin Engineering University, No. 145-1, Nantong Street, Nangang District, Harbin, Heilongjiang, China; National Key Laboratory of Underwater Acoustic Science and Technology, Harbin Engineering University, No. 145-1, Nantong Street, Nangang District, Harbin, Heilongjiang, China},
abstract={Information fusion is a very important technology which can use multisensor network data to get a better performance than single one; therefore, it is widely used in the filed of target recognition, target tracking, automatic control, decision making and so on. However, because of noise and interference, sometimes the sensors may obtain erroneous, inaccurate or heterogeneous data, which will produce the conflict information among different sensors and get the wrong result after information fusion. In this paper, based on the Dempster–Shafer (D–S) theory, we introduce how to set up the model of multisensor network information fusion. And then, we discuss the problem of conflict information fusion in the framework of evidence and several improved methods are introduced. Finally, based on Mahalanobis distance, an improved solution method is presented. The numerical simulation results prove that this new improved method can get the same result as traditional methods, beyond which it can make a reasonable decision with high conflict information. Therefore, this new improved method can be used in the filed of high noise and interference. © 2016, Springer Science+Business Media New York.},
author_keywords={Conflict information;  Dempster–Shafer evidence theory;  Information fusion;  Mahalanobis distance;  Multisensor network},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2016973,
author={Wang, X.-M.a  b  and Liu, W.-Q.a  and Deng, Z.-L.a },
title={Modified robust covariance intersection fusion steady-state Kalman filter for systems with missing measurements and uncertain noise variances},
journal={Kongzhi Lilun Yu Yingyong/Control Theory and Applications},
year={2016},
volume={33},
number={7},
pages={973-979},
doi={10.7641/CTA.2016.50620},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984629213&doi=10.7641%2fCTA.2016.50620&partnerID=40&md5=24605df8d62058da36ad220d21b128a0},
affiliation={Electronic Engineering College, Heilongjiang University, Harbin, Heilongjiang, China; Department of Computer, Heilongjiang Technology and Business College, Harbin, Heilongjiang, China},
abstract={For the linear time-invariant multisensor system with missing measurements and uncertain noise variances, by introducing the fictitious noises, the original system can be converted into one with only uncertain noise variances. According to the minimax robust estimation principle, using the Lyapunov equation approach, the local robust steady-state Kalman filters and the minimal upper bounds of their actual variances are presented, and a modified robust covariance intersection (CI) fusion steady-state Kalman filter and the minimal upper bound of its actual variances are presented using the conservative cross-covariances of the local filtering errors. The robustness of the robust local and fused filters is proved, and it is proved that the robust accuracy of the modified CI fuser is higher than that of the original CI fuser, and higher than that of each local filter. A simulation example verifies correctness and effectiveness of the proposed results. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.},
author_keywords={Covariance intersection (CI) fusion;  Lyapunov equation approach;  Minimax robust Kalman filter;  Missing measurements;  Multisensor system;  Uncertain noise variance},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Habib2016708,
author={Habib, C.a  and Makhoul, A.a  and Darazi, R.b  and Couturier, R.a },
title={Multisensor data fusion and decision support in wireless body sensor networks},
journal={Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium},
year={2016},
pages={708-712},
doi={10.1109/NOMS.2016.7502882},
art_number={7502882},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979781357&doi=10.1109%2fNOMS.2016.7502882&partnerID=40&md5=a8c62a825534327375875cfd604c9aa1},
affiliation={University of Franche-Comté, FEMTO-ST Institute, 19 Av Maréchal Juin, Belfort, France; Antonine University, Faculty of Computer Engineering, Baabda, Lebanon},
abstract={Maintaining and improving the quality of life in ageing populations is a necessity. Hence, distant patient monitoring is a solution providing constant surveillance of vital signs and the detection of emergencies when they occur. In the past few years, wireless body sensor networks (WBSNs) emerged as a low cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measures and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a data fusion model using a decision matrix, an early warning score system and fuzzy set theory. We propose an algorithm at the coordinator level of the WBSN, aiming to take the appropriate decision when an emergency is detected. © 2016 IEEE.},
author_keywords={BSN;  context-awareness;  coordinator;  data fusion;  early warning system;  fuzzy theory},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pereira2016834,
author={Pereira, V. and Monteiro, E. and Sá Silva, J.},
title={A data fusion protocol for WSN performance and data retrieval},
journal={Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium},
year={2016},
pages={834-837},
doi={10.1109/NOMS.2016.7502910},
art_number={7502910},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979765622&doi=10.1109%2fNOMS.2016.7502910&partnerID=40&md5=88dafa0e8e617fac971ea2cf8090451d},
affiliation={CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal},
abstract={The specific capabilities of Wireless Sensor Networks (WSNs), such as fast deployment and flexibility, together with the low cost solutions that can be achieved, bring new opportunities for an all-new range of applications, far from the typical scenarios of low requirements and high redundancy. Some of these new scenarios involve the assurance of predefined performance goals. To guarantee that these goals are achieved, a careful constant monitoring must exist. However, this monitoring depletes nodes energy (especially in middle nodes that have to retransmit the information to the sink), increases interference between nodes and causes an overhead in the used bandwidth, a foreseeable result if measuring performance of a large multilevel WSN. In the protocol to be presented, data fusion is used to enhance the benefits of continuous monitoring while minimizing its implied overhead, using the specific characteristics of WSNs in its favor. © 2016 IEEE.},
author_keywords={fusion;  monitoring;  wireless sensor networks},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cappello2016457,
author={Cappello, F. and Sabatini, R. and Ramasamy, S.},
title={Aircraft dynamics model augmentation for RPAS navigation and guidance},
journal={2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016},
year={2016},
pages={457-464},
doi={10.1109/ICUAS.2016.7502644},
art_number={7502644},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979784072&doi=10.1109%2fICUAS.2016.7502644&partnerID=40&md5=0bd6ae87ec3d223af28367610e4bebab},
affiliation={School of Engineering - Aerospace, Mechanical and Manufacturing, RMIT University, Melbourne, VIC, Australia},
abstract={In this paper, Aircraft Dynamics Model (ADM) augmentation for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. This approach provides additional information suitable to compensate for the shortcomings of vision based navigation sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors for attitude determination tasks. The ADM virtual sensor is essentially a knowledge-based module and is used to augment the navigation state vector by predicting RPAS flight dynamics (aircraft trajectory and attitude motion). The ADM employs a rigid body 6-Degree of Freedom (6-DoF) model and is implemented in integrated multi-sensor data fusion architectures. The integration is accomplished with an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). After introducing the key mathematical models describing the 6-DoF ADM, the sensor and integrated system performance are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope and a preliminary sensitivity analysis is performed. In addition to a centralised filter, a dedicated ADM processor (i.e., a local pre-filter) is adopted to account for the RPAS manoeuvring envelope in different flight phases, in order to extend the ADM validity time across all segments of the RPAS trajectory. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results verify that the ADM virtual sensor provides improved performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved by pre-filtering. © 2016 IEEE.},
author_keywords={Aircraft Dynamics Model;  Knowledge-Based Sesnor;  Multi-Sensor Data Fusion;  Virtual Sensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Yong201654,
author={Yong, X. and Fang, Y. and Wu, Y. and Yang, P.},
title={An asynchronous sensor bias estimation algorithm utilizing targets' positions only},
journal={Information Fusion},
year={2016},
volume={27},
pages={54-63},
doi={10.1016/j.inffus.2015.05.003},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935023662&doi=10.1016%2fj.inffus.2015.05.003&partnerID=40&md5=62af9ddddfeb2e330cd7115ac4e158be},
affiliation={School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an, Shanxi, China},
abstract={Bias estimation is a critical problem in multi-sensor tracking systems, and most existing research has focused on the bias estimation of synchronous sensors; however, in practical applications, sensor measurements are usually asynchronous. The primary contribution of this paper is that a novel algorithm using B-spline interpolation time registration to achieve asynchronous sensor bias estimation is proposed. First, measurements are transformed into synchronous data using the B-spline interpolation time registration method. The time registration results are expressed as weighted results of the measurements. Second, a pseudo measurement equation is created based on the synchronous data. Compared with the pseudo measurements of other algorithms that use weighting coefficients, which are calculated by the target's state, including the target's velocity and time of arrival (TOA), a pseudo measurement that only depends on the target's position can be derived. Thus, the problem of asynchronous sensor bias estimation, particularly with manoeuvring targets, can be solved effectively by the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulations with the target performing s-shaped manoeuvres. Monte Carlo simulation results indicate that the Cramer-Rao lower bound (CRLB) is achievable; thus, the proposed algorithm is statistically efficient. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Asynchronous sensors;  Bias estimation;  Data fusion;  Manoeuvring targets;  Spatial registration},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Sun2016452,
author={Sun, X.J. and Yan, G.M. and Zhang, B.},
title={Fractional order Kalman fuser},
journal={2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2015},
year={2016},
pages={452-456},
doi={10.1109/ICCWAMTIP.2015.7494029},
art_number={7494029},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978792857&doi=10.1109%2fICCWAMTIP.2015.7494029&partnerID=40&md5=97fee47261c8e2b7a4308f90820acf6b},
affiliation={Department of Automation, Heilongjiang University, Harbin, China},
abstract={Based on the measurement fusion algorithm, a fractional order Kalman fuser is presented for the multisensor linear discrete time-varying fractional state-space systems with different measurement matrices and uncorrelated noises. It is rigorously proved that the fuser is numerically identical to the centralized fusion fractional order Kalman estimator, so that it has the global optimality. The simulation example for a 3-sensors time-varying 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{Khudonogova2016,
author={Khudonogova, L.I. and Muravyov, S.V.},
title={Energy-accuracy aware active node selection in wireless sensor networks},
journal={2016 International Siberian Conference on Control and Communications, SIBCON 2016 - Proceedings},
year={2016},
doi={10.1109/SIBCON.2016.7491835},
art_number={7491835},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978034011&doi=10.1109%2fSIBCON.2016.7491835&partnerID=40&md5=0b52ea8331deae9c858508b29462118c},
affiliation={Dept. of Computer-Aided Measurement Systems and Metrology, National Research Tomsk Polytechnic University, Tomsk, Russian Federation},
abstract={One of the main issues in wireless sensor networks (WSNs) is energy conservation due to limited power of sensor node battery. To save energy, WSNs operate in duty-cycling work mode, when the sensor nodes alternate between active and dormant states. In this paper, we propose an active node set selection algorithm (ANSS), based on preference aggregation. The aim of the algorithm is to reduce the number of active sensor nodes, thus decreasing energy consumption while keeping required measurement accuracy level. Nodes, included in the active node set, are selected on the base of such parameters as energy consumption, distance to a cluster head, and multisensor general accuracy. © 2016 IEEE.},
author_keywords={ecological monitoring;  interval voting;  Kemeny ranking;  multisensor data fusion;  preference aggregation;  wireless sensor network},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hao20161461,
author={Hao, X.a  and Li, F.b  and Gao, X.b },
title={Construction of information fusion system based on cloud computing},
journal={Proceedings of 2015 4th International Conference on Computer Science and Network Technology, ICCSNT 2015},
year={2016},
pages={1461-1465},
doi={10.1109/ICCSNT.2015.7491004},
art_number={7491004},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979306579&doi=10.1109%2fICCSNT.2015.7491004&partnerID=40&md5=63fc6dc88564caf98ea122cf200bc4b6},
affiliation={Shenyang Aircraft Design Institute, Shenyang, China; School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China},
abstract={With the development of military technology, the large scale war, network war and the Group war have gradually become the main form of future wars. This brings a new demand of large scale multi-sensor data fusion and supercomputing capacity for information fusion system. Aiming at the shortcomings of traditional information fusion system, a new information fusion system is designed based on cloud computing. The functional module and software architecture of the new information fusion system are presented then. At the end of this paper, the operational workflow of the system under a certain operation scenario is analyzed. Compared with the traditional information fusion system, the cloud-based information fusion system has superiorities in data throughput, computing capability and system flexibility. It can meet the needs of future war and provides a theoretical support for the building of information fusion system of the future. © 2015 IEEE.},
author_keywords={cloud computing;  information fusion;  map-reduce},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Guo2016548,
author={Guo, Q. and Huang, J.},
title={A complex event processing based approach of multi-sensor data fusion in IoT sensing systems},
journal={Proceedings of 2015 4th International Conference on Computer Science and Network Technology, ICCSNT 2015},
year={2016},
pages={548-551},
doi={10.1109/ICCSNT.2015.7490807},
art_number={7490807},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979231445&doi=10.1109%2fICCSNT.2015

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