Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion

In distributed estimation fusion, processed data from each sensor is sent to the fusion center. By taking linear transformation of the raw measurements of each sensor, two optimal distributed fusion algorithms are proposed in this paper. Compared with existing fusion algorithms, they have three nice properties. First, they are optimal in the sense that they are equivalent to the optimal centralized fusion. Second, their communication requirements from each sensor to the fusion center are equal to or less than those of the centralized and most existing distributed fusion algorithms. Third, they do not need the inverses of estimation error covariance matrices, which are assumed to exist in most existing algorithms but can not be guaranteed to exist. So the proposed algorithms can be applied in more cases. Pros and cons of these two new algorithms are analyzed. A possible way to reduce the computational complexity of the new algorithms, an extension to the case of a singular covariance matrix of measurement noise, and an extension to the reduced-rate communication case for some simple systems are also discussed.

[1]  Sumit Roy,et al.  Decentralized structures for parallel Kalman filtering , 1988 .

[2]  Chongzhao Han,et al.  Optimal linear estimation fusion .I. Unified fusion rules , 2003, IEEE Trans. Inf. Theory.

[3]  X. Rong Li.  Optimal linear estimation fusion-part VII: dynamic systems , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[4]  X. R. Li,et al.  Optimal Linear Estimation Fusion — Part IV : Optimality and Efficiency of Distributed Fusion , 2001 .

[5]  D. Musicki,et al.  Multi-target tracking using multiple passive bearings-only asynchronous sensors , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[6]  X. Rong Li,et al.  Recursibility and optimal linear estimation and filtering , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[7]  Zhansheng Duan,et al.  The optimality of a class of distributed estimation fusion algorithm , 2008, 2008 11th International Conference on Information Fusion.

[8]  Zhi-Quan Luo,et al.  Distributed Estimation Using Reduced-Dimensionality Sensor Observations , 2005, IEEE Transactions on Signal Processing.

[9]  P. L. Odell,et al.  Full Rank Factorization of Matrices , 1999 .

[10]  L.P. Yan,et al.  Asynchronous multirate multisensor information fusion algorithm , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Yunmin Zhu,et al.  Optimal Kalman filtering fusion with cross-correlated sensor noises , 2007, Autom..

[12]  Yih-Fang Huang,et al.  Source Localization and Tracking Using Distributed Asynchronous Sensors , 2006, IEEE Transactions on Signal Processing.

[13]  Thiagalingam Kirubarajan,et al.  Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion] , 2003 .

[14]  J.E. Gray,et al.  Theory of distributed estimation using multiple asynchronous sensors , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Chongzhao Han,et al.  Optimal Linear Estimation Fusion — Part I : Unified Fusion Rules , 2001 .

[16]  Zhansheng Duan,et al.  Optimal distributed estimation fusion with transformed data , 2008, 2008 11th International Conference on Information Fusion.

[17]  Zhansheng Duan,et al.  Optimal distributed estimation fusion with compressed data , 2009, 2009 12th International Conference on Information Fusion.

[18]  Peng Zhang,et al.  Optimal linear estimation fusion - part VI: sensor data compression , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[19]  T. Kirubarajan,et al.  Multisensor multitarget bias estimation for general asynchronous sensors , 2005, IEEE Transactions on Aerospace and Electronic Systems.

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

[21]  S. Mori,et al.  Performance evaluation for MAP state estimate fusion , 2004 .

[22]  Oliver E. Drummond,et al.  Feedback in track fusion without process noise , 1995, Optics & Photonics.

[23]  Chee-Yee Chong,et al.  Track association and track fusion with nondeterministic target dynamics , 2002 .

[24]  Xin Tian,et al.  Exact algorithms for four track-to-track fusion configurations: All you wanted to know but were afraid to ask , 2009, 2009 12th International Conference on Information Fusion.

[25]  Kuo-Chu Chang,et al.  Architectures and algorithms for track association and fusion , 2000 .

[26]  Yunmin Zhu,et al.  Sensors' optimal dimensionality compression matrix in estimation fusion , 2005, Autom..

[27]  K. H. Kim,et al.  Development of track to track fusion algorithms , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[28]  Yunmin Zhu,et al.  Optimal dimensionality reduction of sensor data in multisensor estimation fusion , 2005, IEEE Trans. Signal Process..

[29]  Chongzhao Han,et al.  Optimal batch asynchronous fusion algorithm , 2005, IEEE International Conference on Vehicular Electronics and Safety, 2005..

[30]  Y. Bar-Shalom,et al.  On optimal track-to-track fusion , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[31]  Yaakov Bar-Shalom,et al.  The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[32]  Y. Bar-Shalom.  On the track-to-track correlation problem , 1981 .

[33]  Zhi Tian,et al.  Performance evaluation of track fusion with information matrix filter , 2002 .