Onboard Information Fusion for Multisatellite Collaborative Observation: Summary, challenges, and perspectives

Onboard information fusion for multisatellites, which is based on spatial computing mode, can improve the satellites’ capability, such as the spatial–temporal coverage, detection accuracy, recognition confidence, position precision, and prediction precision for disaster monitoring, maritime surveillance, and other emergent or continuous persistent observing situations. First, we analyze the necessity of onboard information fusion. Next, the recent onboard processing developments are summarized and the existing problems are discussed. Furthermore, the key technologies and concepts of onboard information fusion are summarized in the fields of feature representation, association, feature-level fusion, spatial computing architecture, and other issues. Finally, the future developments of onboard information fusion are investigated and discussed.

[1]  K. Wagstaff,et al.  Resource Consumption and Radiation Tolerance Assessment for Data Analysis Algorithms Onboard Spacecraft , 2022, IEEE Transactions on Aerospace and Electronic Systems.

[2]  D. Heynderickx,et al.  First Results and Analysis From ESA Next Generation Radiation Monitor Unit Onboard EDRS-C , 2022, IEEE Transactions on Nuclear Science.

[3]  Wei Li,et al.  Hyperspectral and SAR Image Classification via Multiscale Interactive Fusion Network , 2022, IEEE Transactions on Neural Networks and Learning Systems.

[4]  P. Jain,et al.  Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Wei Li,et al.  Asymmetric Feature Fusion Network for Hyperspectral and SAR Image Classification , 2022, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Xiao Xiang Zhu,et al.  Change Detection Meets Visual Question Answering , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Mingsen Lin,et al.  Global Ocean Chlorophyll-a Concentrations Derived From COCTS Onboard the HY-1C Satellite and Their Preliminary Evaluation , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Lingli Zhao,et al.  Low-Resolution Fully Polarimetric SAR and High-Resolution Single-Polarization SAR Image Fusion Network , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Yong Liu,et al.  Information Fusion of GF-1 and GF-4 Satellite Imagery for Ship Surveillance , 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

[10]  Ziang Cao,et al.  Onboard Real-Time Aerial Tracking With Efficient Siamese Anchor Proposal Network , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Yan Wang,et al.  Blocked Azimuth Spectrum Reconstruction Algorithm for Onboard Real-Time Dual-Channel SAR Imaging , 2021, IEEE Geoscience and Remote Sensing Letters.

[12]  M. Xing,et al.  Artificial Intelligence In Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review , 2021, IEEE Geoscience and Remote Sensing Magazine.

[13]  Chih-Heng Lu,et al.  Pixel Offset Fusion of SAR and Optical Images for 3-D Coseismic Surface Deformation , 2021, IEEE Geoscience and Remote Sensing Letters.

[14]  Massimo Bonavita,et al.  Machine Learning for Earth System Observation and Prediction , 2021, Bulletin of the American Meteorological Society.

[15]  Wei Wang,et al.  A Novel Weight Generator in Real-Time Processing Architecture of DBF-SAR , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Debora Pastina,et al.  Passive Radar Imaging of Ship Targets With GNSS Signals of Opportunity , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Myoung-Hwan Ahn,et al.  Synergistic Benefits of Intercomparison Between Simulated and Measured Radiances of Imagers Onboard Geostationary Satellites , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Ronghua Ma,et al.  Improved Radiometric and Spatial Capabilities of the Coastal Zone Imager Onboard Chinese HY-1C Satellite for Inland Lakes , 2021, IEEE Geoscience and Remote Sensing Letters.

[19]  Mengdao Xing,et al.  Multisystem Interferometric Data Fusion Framework: A Three-Step Sensing Approach , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Yu Wang,et al.  First Year On-Orbit Calibration of the Chinese Environmental Trace Gas Monitoring Instrument Onboard GaoFen-5 , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Fabio Del Frate,et al.  SAR Data Fusion Using Nonlinear Principal Component Analysis , 2020, IEEE Geoscience and Remote Sensing Letters.

[22]  Altamiro Amadeu Susin,et al.  A Hardware Accelerator for Onboard Spatial Resolution Enhancement of Hyperspectral Images , 2020, IEEE Geoscience and Remote Sensing Letters.

[23]  Roger Skjetne,et al.  Ice Condition Assessment Using Onboard Accelerometers and Statistical Change Detection , 2020, IEEE Journal of Oceanic Engineering.

[24]  Kenji Takasaki,et al.  Illuminating dark fishing fleets in North Korea , 2020, Science Advances.

[25]  Wenming Lin,et al.  Validation of New Sea Surface Wind Products From Scatterometers Onboard the HY-2B and MetOp-C Satellites , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Cewu Lu,et al.  Towards a new generation of artificial intelligence in China , 2020, Nature Machine Intelligence.

[27]  J. L. Garry,et al.  Real-Time Detection and Filtering of Radio Frequency Interference Onboard a Spaceborne Microwave Radiometer: The CubeRRT Mission , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Jonathan Cheung-Wai Chan,et al.  Thick Cloud Removal With Optical and SAR Imagery via Convolutional-Mapping-Deconvolutional Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Faisal Hossain,et al.  A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Tomasz Malisiewicz,et al.  SuperGlue: Learning Feature Matching With Graph Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Ling Li,et al.  Design and Implementation of On-orbit Valuable Image Extraction for the TianZhi-1 Satellite , 2019, 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[32]  Enrico Magli,et al.  High-Throughput Onboard Hyperspectral Image Compression With Ground-Based CNN Reconstruction , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Gerhard Krieger,et al.  Efficient Onboard Quantization for Multichannel SAR Systems , 2019, IEEE Geoscience and Remote Sensing Letters.

[34]  Xiang Zhou,et al.  A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching , 2018, Sensors.

[35]  Alan D. George,et al.  Onboard Processing With Hybrid and Reconfigurable Computing on Small Satellites , 2018, Proceedings of the IEEE.

[36]  Barbara A. Block,et al.  Tracking the global footprint of fisheries , 2018, Science.

[37]  Jiang Yonghua,et al.  Satellite Video Point-target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature , 2017 .

[38]  Fawzi Nashashibi,et al.  Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks , 2017, ESANN.

[39]  Jianquan Liu,et al.  Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition , 2016, Int. J. Semantic Comput..

[40]  Yanning Zhang,et al.  Small Moving Vehicle Detection in a Satellite Video of an Urban Area , 2016, Sensors.

[41]  Steven K. Feiner,et al.  From GPS and virtual globes to spatial computing - 2020 , 2015, GeoInformatica.

[42]  Huanxin Zou,et al.  Combined Use of Optical Imaging Satellite Data and Electronic Intelligence Satellite Data for Large Scale Ship Group Surveillance , 2015 .

[43]  Jocelyn Chanussot,et al.  Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing , 2014, Proceedings of the IEEE.

[44]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Dong Yu,et al.  Tensor Deep Stacking Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  YuDong,et al.  Tensor Deep Stacking Networks , 2013 .

[47]  Huanxin Zou,et al.  Multimodal Remote Sensing Data Fusion via Coherent Point Set Analysis , 2013, IEEE Geoscience and Remote Sensing Letters.

[48]  Jeff A. Bilmes,et al.  Deep Canonical Correlation Analysis , 2013, ICML.

[49]  Dong Yu,et al.  The Deep Tensor Neural Network With Applications to Large Vocabulary Speech Recognition , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[50]  Byrav Ramamurthy,et al.  Communication Technologies and Architectures for Space Network and Interplanetary Internet , 2013, IEEE Communications Surveys & Tutorials.

[51]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[52]  Jin Li,et al.  [Lossless compression of hyperspectral image for space-borne application]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[53]  Gwendoline Blanchet,et al.  PLEIADES HR IN FLIGHT GEOMETRICAL CALIBRATION : LOCATION AND MAPPING OF THE FOCAL PLANE , 2012 .

[54]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[55]  Thomas Fritz,et al.  Ship Surveillance With TerraSAR-X , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[56]  John Shawe-Taylor,et al.  Sparse canonical correlation analysis , 2009, Machine Learning.

[57]  Pengfei Shi,et al.  A Novel Method of Combined Feature Extraction for Recognition , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[58]  Philip S. Yu,et al.  A General Model for Multiple View Unsupervised Learning , 2008, SDM.

[59]  Yi-Nung Chung,et al.  Multiple-Target Tracking with Competitive Hopfield Neural Network Based Data Association , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[60]  Seungjin Choi,et al.  Two-Dimensional Canonical Correlation Analysis , 2007, IEEE Signal Processing Letters.

[61]  Songcan Chen,et al.  Locality preserving CCA with applications to data visualization and pose estimation , 2007, Image Vis. Comput..

[62]  Uday B. Desai,et al.  Robust Neural-Network-Based Data Association and Multiple Model-Based Tracking of Multiple Point Targets , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[63]  He You,et al.  New track correlation algorithms in a multisensor data fusion system , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[64]  Shotaro Akaho,et al.  A kernel method for canonical correlation analysis , 2006, ArXiv.

[65]  D. Boyer,et al.  Mission operations of Earth Observing-1 with onboard autonomy , 2006, 2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06).

[66]  Les Doggrell,et al.  Operationally Responsive Space: A Vision for the Future of Military Space , 2006 .

[67]  Naouma Kourti,et al.  FINDINGS OF THE DECLIMS PROJECT - DETECTION AND CLASSIFICATION OF MARINE TRAFFIC FROM SPACE , 2006 .

[68]  G. Roberts,et al.  Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001–2004) , 2006 .

[69]  Taek Lyul Song,et al.  A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment , 2005, Signal Process..

[70]  Aaron D. Lanterman,et al.  Probability hypothesis density-based multitarget tracking with bistatic range and Doppler observations , 2005 .

[71]  David Hardy,et al.  The PROBA-1 microsatellite , 2004, SPIE Optics + Photonics.

[72]  K. Punithakumar,et al.  Multiple-model probability hypothesis density filter for tracking maneuvering targets , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[73]  Jangsun Baek,et al.  Face recognition using partial least squares components , 2004, Pattern Recognit..

[74]  Y. Bar-Shalom,et al.  Tracking with classification-aided multiframe data association , 2003, IEEE Transactions on Aerospace and Electronic Systems.

[75]  Jian Yang,et al.  Feature fusion: parallel strategy vs. serial strategy , 2003, Pattern Recognit..

[76]  B. J. Fraser.  The FedSat Microsatellite Mission , 2003 .

[77]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[78]  James L. Rash,et al.  Internet technology for future space missions , 2002, Comput. Networks.

[79]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[80]  Thomas R. Caudill,et al.  MightySat II.1 hyperspectral imager: summary of on-orbit performance , 2002, SPIE Optics + Photonics.

[81]  Robert D. Smith,et al.  NEMO satellite sensor imaging payload , 1998, Optics & Photonics.

[82]  Pramod K. Varshney,et al.  Multisensor Data Fusion , 1997, IEA/AIE.

[83]  Lucien Wald,et al.  An overview of concepts in fusion of Earth data , 1997 .

[84]  Hugh Durrant-Whyte,et al.  Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach , 1995 .

[85]  Robin J. Evans,et al.  Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..

[86]  J. A. Roecker.  A class of near optimal JPDA algorithms , 1994 .

[87]  J. A. Roecker,et al.  Suboptimal joint probabilistic data association , 1993 .

[88]  R. Danchick,et al.  A fast method for finding the exact N-best hypotheses for multitarget tracking , 1993 .

[89]  Yaakov Bar-Shalom,et al.  Interacting multiple model tracking with target amplitude feature , 1993 .

[90]  Krishna R. Pattipati,et al.  A Multisensor-Multitarget Data Association Algorithm for Heterogeneous Sensors , 1992, 1992 American Control Conference.

[91]  Y. Bar-Shalom,et al.  A new relaxation algorithm and passive sensor data association , 1992 .

[92]  Y. Bar-Shalom,et al.  Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm , 1989 .

[93]  E. Sein,et al.  An experimental optical link between an earth remote sensing satellite, spot 4, and a European data relay satellite , 1988 .

[94]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[95]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[96]  R. Singer,et al.  New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments , 1973 .

[97]  B. Hou,et al.  Self-Distillation Feature Learning Network for Optical and SAR Image Registration , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[98]  Mingyu Lu,et al.  Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[99]  Siyu Zhu,et al.  FY4QPE-MSA: an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis from AGRI onboard Chinese FY-4 series satellites , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[100]  Hai-bo Li,et al.  A fusion method using terrestrial laser scanning and unmanned aerial vehicle photogrammetry for landslide deformation monitoring under complex terrain conditions , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[101]  Hao Liu,et al.  Performance Simulation of the Payload IMR and MICAP Onboard the Chinese Ocean Salinity Satellite , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[102]  Zhaoxiang Zhang,et al.  Unsupervised SAR and Optical Image Matching Using Siamese Domain Adaptation , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[103]  H. S. Gusain,et al.  Image Fusion of Ku-Band-Based SCATSAT-1 and MODIS Data for Cloud-Free Change Detection Over Western Himalayas , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[104]  M. Xing,et al.  CANet: An Unsupervised Deep Convolutional Neural Network for Efficient Cluster-Analysis-Based Multibaseline InSAR Phase Unwrapping , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[105]  Shuang-xi Zhang,et al.  Distributed Real-Time Image Processing of Formation Flying SAR Based on Embedded GPUs , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[106]  Jun Yu Li,et al.  Vicarious Radiometric Calibration of the AHSI Instrument Onboard ZY1E on Dunhuang Radiometric Calibration Site , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[107]  Guo Zhang,et al.  Combined Adjustment Pipeline for Improved Global Geopositioning Accuracy of Optical Satellite Imagery With the Aid of SAR and GLAS , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[108]  M. Ramesh,et al.  Review of Landslide Monitoring Techniques With IoT Integration Opportunities , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[109]  Zhengqiang Li,et al.  In-orbit Test of the Polarized Scanning Atmospheric Corrector (PSAC) onboard Chinese Environmental Protection and Disaster Monitoring Satellite Constellation HJ-2 A/B , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[110]  Zhengqiang Li,et al.  Geolocation Error Estimation Method for the Wide Swath Polarized Scanning Atmospheric Corrector Onboard HJ-2 A/B Satellites , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[111]  M. Xing,et al.  Comparative Study of DEM Reconstruction Accuracy Between Single- and Multibaseline InSAR Phase Unwrapping , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[112]  Junsheng Li,et al.  A Classification-Based, Semianalytical Approach for Estimating Water Clarity From a Hyperspectral Sensor Onboard the ZY1-02D Satellite , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[113]  A. Marino,et al.  Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[114]  M. Xing,et al.  Deep Learning-Based Branch-Cut Method for InSAR Two-Dimensional Phase Unwrapping , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[115]  L. Bruzzone,et al.  Self-Supervised SAR-Optical Data Fusion of Sentinel-1/-2 Images , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[116]  A. Marino,et al.  Learning-Based Tracking of Crop Biophysical Variables and Key Dates Estimation From Fusion of SAR and Optical Data , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[117]  H. You,et al.  CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[118]  Zhenman Fang,et al.  Algorithm/Hardware Co-Design for Real-Time On-Satellite CNN based Ship Detection in SAR Imagery , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[119]  Y. Ye,et al.  Phase Congruency Order-Based Local Structural Feature for SAR and Optical Image Matching , 2022, IEEE Geoscience and Remote Sensing Letters.

[120]  Pedro R. Miranda,et al.  Onboard Processing of Synthetic Aperture Radar Backprojection Algorithm in FPGA , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[121]  T. Zeng,et al.  A High-Frequency Motion Error Compensation Algorithm based on Multiple Errors Separation in BiSAR onboard Mini-UAVs , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[122]  A. Marino,et al.  A General Framework of Polarimetric Detectors Based on Quadratic Optimization , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[123]  M. Xing,et al.  PU-GAN: A One-Step 2-D InSAR Phase Unwrapping Based on Conditional Generative Adversarial Network , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[124]  Rémy Fieuzal,et al.  Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[125]  Lifan Zhou,et al.  PDNet: A Lightweight Deep Convolutional Neural Network for InSAR Phase Denoising , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[126]  Bin Zou,et al.  Sea Surface Temperatures Derived From COCTS Onboard the HY-1C Satellite , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[127]  Yuchen Li,et al.  Performance Evaluation of Deception Against Synthetic Aperture Radar Based on Multifeature Fusion , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[128]  William Perrie,et al.  Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[129]  Peng Wang,et al.  On-Board Fast and Intelligent Perception of Ships With the “Jilin-1” Spectrum 01/02 Satellites , 2020, IEEE Access.

[130]  Z. Zhao,et al.  A novel method of ship detection by combining space-borne SAR and GNSS-R , 2020, IET International Radar Conference (IET IRC 2020).

[131]  Laurence T. Yang,et al.  Deep Computation Model for Unsupervised Feature Learning on Big Data , 2016, IEEE Transactions on Services Computing.

[132]  Jiang Hui-li,et al.  Several Key Problems of Space-ground Integration Information Network , 2014 .

[133]  Min Shiqua,et al.  An Idea of China's Space-based Integrated Information Network , 2013 .

[134]  郭永飞 Guo Yong-fei,et al.  Composition and compression of satellite multi-channel remote sensing images , 2013 .

[135]  Zhang Bing,et al.  Intelligent remote sensing satellite system , 2011, National Remote Sensing Bulletin.

[136]  Yuan Jianping,et al.  On-Orbit Autonomous Operation Cooperative Control of Multi-spacecraft Formation , 2010 .

[137]  Sunduz Keles,et al.  Sparse Partial Least Squares Classification for High Dimensional Data , 2010, Statistical applications in genetics and molecular biology.

[138]  Su Yi,et al.  A MSA Feature-based Multiple Targets Association Algorithm in Remote Sensing Images , 2008, National Remote Sensing Bulletin.

[139]  Lin Jin,et al.  Using Target's Polarization for Data Association in Multiple Target Tracking , 2006, 2006 8th international Conference on Signal Processing.

[140]  Xu Shou-shi,et al.  A united target data association algorithm based on D-S theory and multiple remote sensing images , 2006 .

[141]  Shen Xin,et al.  On intelligent earth observation systems , 2005 .

[142]  Nageswara S. V. Rao,et al.  Information fusion methods based on physical laws , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[143]  Luo Jing-qing,et al.  Passive tracking based on data association with information fusion of multi-feature and multi-target , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[144]  Guoqing Zhou.  Architecture of Future Intelligent Earth Observing Satellites ( FIEOS ) in 2010 and Beyond , 2001 .

[145]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[146]  A. La.  Association Algorithm of Data Fusion Using Doppler Frequency of Targets , 1999 .

[147]  Colin Fyfe,et al.  Canonical correlation analysis using artificial neural networks , 1998, ESANN.

[148]  I. R. Goodman,et al.  Mathematics of Data Fusion , 1997 .

[149]  Hsueh-Jyh Li,et al.  Using range profiles for data association in multiple-target tracking , 1996 .

[150]  Hsueh-Jyh Li,et al.  Using range profiles for data association in multiple-target tracking , 1996 .

[151]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[152]  H. P. Blom.  Overlooked potential of systems with Markovian coefficients , 1986, 1986 25th IEEE Conference on Decision and Control.

[153]  D. Reid.  An algorithm for tracking multiple targets , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.