Multiframe detector/tracker: optimal performance

We develop the optimal Bayes multiframe detector/tracker for rigid extended targets that move randomly in clutter. The performance of this optimal algorithm provides a bound on the performance of any other suboptimal detector/tracker. We determine by Monte Carlo simulations the optimal performance under a variety of scenarios including spatially correlated Gaussian clutter and non-Gaussian (K and Weibull) clutter. We show that, for similar tracking performance, the optimal Bayes tracker can achieve peak signal-to-noise ratio gains possibly larger than 10 dB over the commonly used combination of a spatial matched filter (spatial correlator) and a linearized Kalman-Bucy tracker. Simulations using real clutter data with a simulated target suggest similar performance gains when the clutter model parameters are unknown and estimated from the measurements.

[1]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

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

[3]  A. Öztürk,et al.  Non-Gaussian random vector identification using spherically invariant random processes , 1993 .

[4]  Alexander Graham,et al.  Kronecker Products and Matrix Calculus: With Applications , 1981 .

[5]  Nikhil Balram,et al.  Recursive structure of noncausal Gauss-Markov random fields , 1992, IEEE Trans. Inf. Theory.

[6]  C. J. Oliver.  Representation of radar sea clutter , 1988 .

[7]  R. Larson,et al.  A dynamic programming approach to trajectory estimation , 1966 .

[8]  Muralidhar Rangaswamy,et al.  Computer generation of correlated non-Gaussian radar clutter , 1995 .

[9]  Marcelo G. S. Bruno,et al.  The Optimal 2D Multiframe Detector/Tracker , 1999 .

[10]  José M. F. Moura,et al.  Hyperspectral imagery: Clutter adaptation in anomaly detection , 2000, IEEE Trans. Inf. Theory.

[11]  E. Conte,et al.  Characterization of radar clutter as a spherically invariant random process , 1987 .

[12]  T. Musha,et al.  Weibull-Distributed Ground Clutter , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[13]  S. C. Pohlig,et al.  An algorithm for detection of moving optical targets , 1989 .

[14]  R. E. Bethel,et al.  A PDF multitarget tracker , 1994 .

[15]  Yaakov Bar-Shalom,et al.  Adaptive Detection Threshold Optimization for Tracking in Clutter , 1984, 1984 American Control Conference.

[16]  R.M. Gagliardi,et al.  Application of Three-Dimensional Filtering to Moving Target Detection , 1983, IEEE Transactions on Aerospace and Electronic Systems.

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

[18]  J. Jao.  Amplitude distribution of composite terrain radar clutter and the κ-Distribution , 1984 .

[19]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[20]  Marco Lops,et al.  Modelling and simulation of non-Rayleigh radar clutter , 1991 .

[21]  José M. F. Moura,et al.  DCT/DST and Gauss-Markov fields: conditions for equivalence , 1998, IEEE Trans. Signal Process..

[22]  J. Bounds.  The infrared airborne radar sensor suite , 1996 .

[23]  I. Reed,et al.  A Detection Algorithm for Optical Targets in Clutter , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Yair Barniv,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[25]  John W. Woods,et al.  Two-dimensional discrete Markovian fields , 1972, IEEE Trans. Inf. Theory.

[26]  N. Balram,et al.  Noncausal Gauss Markov random fields: Parameter structure and estimation , 1993, IEEE Trans. Inf. Theory.

[27]  George Mamic,et al.  Coherent detection of radar signals in G-distributed clutter , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[28]  Jerry L. Eaves,et al.  Principles of Modern Radar , 1987 .