Target tracking in MIMO radar systems: Techniques and performance analysis

In this paper, moving target tracking performance in multiple input multiple output (MIMO) radar systems with distributed antennas and non-coherent processing is studied. Due to the use of multiple, widely distributed antennas, MIMO radar architectures support both centralized and decentralized tracking techniques. Each receiving radar may contribute to central processing by providing either raw data or partially/fully processed data. Estimation performance of centralized and decentralized tracking is analyzed through the Bayesian Cramer-Rao bound (BCRB). The BCRB offers insight into the effect of the radars geometric layout, the target location, and propagation path losses on tracking accuracies. It is shown that, with different propagation path loss, the manner in which decentralized estimations are combined in the fusion center effects the overall estimation performance. Two tracking algorithms are proposed, corresponding to respectively, a centralized and decentralized modes of operation. It is demonstrated that communication requirements and processing load may be reduced at a relatively low performance cost. Based on mission needs, the system may use either approach: centralized for high accuracy or decentralized for resource-aware tracking.

[1]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[2]  Alexander M. Haimovich,et al.  A MIMO radar system approach to target tracking , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[3]  Alexander M. Haimovich,et al.  Target Localization Accuracy Gain in MIMO Radar-Based Systems , 2008, IEEE Transactions on Information Theory.

[4]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[5]  B. Friedlander,et al.  Waveform Design for MIMO Radars , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[6]  A. Nehorai,et al.  Information Theoretic Adaptive Radar Waveform Design for Multiple Extended Targets , 2007, IEEE Journal of Selected Topics in Signal Processing.

[7]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[8]  Alexander M. Haimovich,et al.  Target Velocity Estimation and Antenna Placement for MIMO Radar With Widely Separated Antennas , 2010, IEEE Journal of Selected Topics in Signal Processing.

[9]  L.J. Cimini,et al.  MIMO Radar with Widely Separated Antennas , 2008, IEEE Signal Processing Magazine.

[10]  Kristine L. Bell,et al.  Bayesian Cramer-Rao bounds for multistatic radar , 2006, 2006 International Waveform Diversity & Design Conference.

[11]  Alexander M. Haimovich,et al.  Spatial Diversity in Radars—Models and Detection Performance , 2006, IEEE Transactions on Signal Processing.

[12]  Lawrence D. Stone,et al.  Bayesian Multiple Target Tracking , 1999 .

[13]  Rick S. Blum,et al.  Concepts and Applications of a MIMO Radar System with Widely Separated Antennas , 2009 .

[14]  Rick S. Blum,et al.  Target tracking in widely separated non-coherent multiple-input multiple-output radar systems , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[15]  P. P. Vaidyanathan,et al.  Properties of the MIMO radar ambiguity function , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Kristine L. Bell,et al.  Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking , 2007 .

[17]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .