Two fusion predictors for discrete-time linear systems with different types of observations

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. Then, the accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI model with multisensor environment.

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

[2]  Yaakov Bar-Shalom,et al.  A multisensor-multitarget data association algorithm for heterogeneous sensors , 1993 .

[3]  Chee-Yee Chong,et al.  Distributed Tracking in Distributed Sensor Networks , 1986 .

[4]  Tae-Sun Choi,et al.  Generalized Millman's formula and its application for estimation problems , 2006, Signal Process..

[5]  Ali T. Alouani.  Distributed estimators for nonlinear systems , 1990 .

[6]  F. Lewis.  Optimal Estimation: With an Introduction to Stochastic Control Theory , 1986 .

[7]  Yaakov Bar-Shalom,et al.  Multitarget-multisensor tracking: Advanced applications , 1989 .

[8]  Yuan Gao,et al.  New approach to information fusion steady-state Kalman filtering , 2005, Autom..

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

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

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

[12]  Yunmin Zhu,et al.  An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

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

[14]  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.

[15]  Shuli Sun,et al.  Optimal and self-tuning information fusion Kalman multi-step predictor , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Shu-li Sun,et al.  Multi-sensor optimal information fusion Kalman filters with applications , 2004 .

[17]  Yunmin Zhu,et al.  The optimality of Kalman filtering fusion with cross-correlated sensor noises , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[18]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Applications and Advances , 1992 .

[19]  Shu-Li Sun,et al.  Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises , 2005 .

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

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

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

[23]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking , 1995 .

[24]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .