Multiple model tracker based on Gaussian mixture reduction for maneuvering targets in clutter

The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Research reported previously reveals that a tracker using an integral square error (ISE) based mixture reduction algorithm can provide performance which is significantly better than any other known techniques using similar numbers of mixture components. One useful algorithm architecture for targets exhibiting very different trajectory characteristics over time would replace each Kalman filter within a conventional MMAE or IMM with an ISE-based algorithm that assumes the adequacy of the same particular dynamics model and discretized parameter choice ("mode"). The performance of such algorithms is evaluated, and compared to that of the corresponding MMAE or IMM based on Kalman filters in the same scenario except for being clutter-free.

[1]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[2]  Michael I. Jordan,et al.  An Introduction to Graphical Models , 2001 .

[3]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[4]  P. Gehler,et al.  An introduction to graphical models , 2001 .

[5]  Jason L. Williams,et al.  Cost-function-based hypothesis control techniques for multiple hypothesis tracking , 2006, Math. Comput. Model..

[6]  J. Bather,et al.  Mixture Reduction Algorithms for Uncertain Tracking , 1988 .

[7]  P. S. Maybeck,et al.  Cost-function-based gaussian mixture reduction for target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[8]  Jun S. Liu,et al.  Mixture Kalman filters , 2000 .

[9]  David J. Salmond.  Mixture reduction algorithms for target tracking in clutter , 1990 .

[10]  Jason L. Williams.  Gaussian Mixture Reduction for Tracking Multiple Maneuvering Targets in Clutter , 2003 .

[11]  D. Salmond.  Tracking in Uncertain Environments , 1989 .

[12]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[13]  A. Izenman.  Recent Developments in Nonparametric Density Estimation , 1991 .

[14]  J.S. Goldstein,et al.  Introduction to radar systems third edition [Book Review] , 2001, IEEE Aerospace and Electronic Systems Magazine.

[15]  D. Magill.  Optimal adaptive estimation of sampled stochastic processes , 1965 .

[16]  D. Salmond.  Mixture reduction algorithms for target tracking , 1989 .

[17]  Y. Bar-Shalom.  Tracking and data association , 1988 .

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