Optimal Linear Estimation Fusion — Part I : Unified Fusion Rules

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.

[1]  S. Grime,et al.  Data fusion in decentralized sensor networks , 1994 .

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

[3]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[4]  Robert J. Fitzgerald.  Analytic track solutions with range-dependent errors , 2000, IEEE Trans. Aerosp. Electron. Syst..

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

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

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

[8]  X. Rong Li.  Unified Optimal L inear Estimation Fusion — Part I I : Discussions and Examples , 2000 .

[9]  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.

[10]  Unified Optimal Linear Estimation Fusion — Part I : Unified Models and Fusion Rules , 2000 .

[11]  X. R. Li,et al.  Optimal Linear Estimation Fusion — Part III : Cross-Correlation of Local Estimation Errors , 2001 .

[12]  Oliver E. Drummond,et al.  Tracklets and a hybrid fusion with process noise , 1997, Optics & Photonics.

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

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

[15]  Yunmin Zhu.  Best Linear Unbiased Estimation Fusion , 1999 .

[16]  Adi Ben-Israel,et al.  Generalized inverses: theory and applications , 1974 .

[17]  X. R. Li,et al.  Optimal Linear Estimation Fusion for Multisensor Dynamic Sy stems , 2003 .

[18]  Ali T. Alouani,et al.  Distributed estimation: constraints on the choice of the local models , 1988 .

[19]  Yunmin Zhu,et al.  The optimality for the distributed Kalman filtering fusion with feedback , 2001, Autom..

[20]  X. R. Li,et al.  Recursive least squares with linear constraints , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[21]  X. R. Li,et al.  ESTIMATOR'S CREDIBILITY AND ITS MEASURES , 2002 .

[22]  Oliver E. Drummond,et al.  Hybrid sensor fusion algorithm architecture and tracklets , 1997, Optics & Photonics.

[23]  Oliver E. Drummond,et al.  Track fusion with feedback , 1996, Defense, Security, and Sensing.

[24]  Stelios C. A. Thomopoulos,et al.  Distributed Fusion Architectures and Algorithms for Target Tracking , 1997, Proc. IEEE.

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

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

[27]  Yunmin Zhu,et al.  Optimal linear estimation fusion. Part V. Relationships , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[28]  Kai-Yuan Cai,et al.  Multisensor Decision And Estimation Fusion , 2003, The International Series on Asian Studies in Computer and Information Science.