Modeling and simulation for multistatic coherent MIMO radar

Multiple input multiple output (MIMO) radar systems employ multiple transmit and receive elements with transmit elements that have the ability to transmit arbitrary waveforms simultaneously and receive elements that have the ability to process all of the transmitted signals jointly. For ground moving target indication (GMTI) systems, MIMO offers the potential to improve angular resolution and illumination time of the radar and therefore lower the minimum detectable velocity of moving targets. In this paper, we consider GMTI systems consisting of airborne platforms in configurations which include collocated transmit and receive elements on a single platform, distributed transmit/receive elements using multiple platforms, and hybrid arrangements. A multistatic coherent MIMO GMTI model is formulated that consists of multiple spatially distributed, moving, multi-element transmit and receive platforms that form multiple bistatic coherent MIMO pairs. Optimum and adaptive detectors are developed and performance is evaluated via simulation for the multistatic coherent MIMO system as well as each bistatic coherent MIMO pair. A clutter simulation methodology is presented that combines a realistic physics-based bistatic scattering model with a spherically invariant random vector (SIRV) random sample generator.

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

[2]  Daniel W. Bliss,et al.  Clutter covariance matrices for GMTI MIMO radar , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[3]  Joel T. Johnson,et al.  A study of the higher-order small-slope approximation for scattering from a Gaussian rough surface , 2003 .

[4]  Rick S. Blum,et al.  MIMO radar: an idea whose time has come , 2004, Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509).

[5]  Qian He,et al.  MIMO Radar Moving Target Detection in Homogeneous Clutter , 2010, IEEE Transactions on Aerospace and Electronic Systems.

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

[7]  R. Klemm Principles of Space-Time Adaptive Processing , 2002 .

[8]  Daniel W. Bliss,et al.  MIMO Radar Waveform Constraints for GMTI , 2010, IEEE Journal of Selected Topics in Signal Processing.

[9]  D. Bruyere,et al.  Optimum and decentralized detection for multistatic airborne radar , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

[11]  Jun Li,et al.  Bistatic MIMO radar space-time adaptive processing , 2011, 2011 IEEE RadarCon (RADAR).

[12]  Hongbin Li,et al.  Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power , 2011, IEEE Transactions on Signal Processing.

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

[14]  Jun Li,et al.  Multitarget Identification and Localization Using Bistatic MIMO Radar Systems , 2008, EURASIP J. Adv. Signal Process..

[15]  Muralidhar Rangaswamy,et al.  Signaling Strategies for the Hybrid MIMO Phased-Array Radar , 2010, IEEE Journal of Selected Topics in Signal Processing.

[16]  James Ward,et al.  Space-time adaptive processing for airborne radar , 1998 .

[17]  Joseph R. Guerci,et al.  Space-Time Adaptive Processing for Radar , 2003 .

[18]  Daniel R. Fuhrmann,et al.  MIMO Radar Ambiguity Functions , 2006, IEEE Journal of Selected Topics in Signal Processing.

[19]  P. P. Vaidyanathan,et al.  MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions , 2008, IEEE Transactions on Signal Processing.