A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar

In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

[1]  Augusto Aubry,et al.  A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems , 2017, IEEE Transactions on Signal Processing.

[2]  Ying Sun,et al.  A Method for Finding Best Channels in Beam-Space Post-Doppler Reduced-Dimension STAP , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[3]  S. Haykin,et al.  Cognitive radar: a way of the future , 2006, IEEE Signal Processing Magazine.

[4]  R. Ruth,et al.  Stability of dynamical systems , 1988 .

[5]  Raviraj S. Adve,et al.  Knowledge based adaptive processing for ground moving target indication , 2007, Digit. Signal Process..

[6]  Teng Long,et al.  Fast STAP Method Based on PAST with Sparse Constraint for Airborne Phased Array Radar , 2016, IEEE Transactions on Signal Processing.

[7]  Karl Gerlach,et al.  Fast converging adaptive processor or a structured covariance matrix , 2000, IEEE Trans. Aerosp. Electron. Syst..

[8]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[9]  Wei Zhang,et al.  Reduced-dimension space-time adaptive processing based on angle-Doppler correlation coefficient , 2016, EURASIP J. Adv. Signal Process..

[10]  Muralidhar Rangaswamy,et al.  Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds , 2005, IEEE Transactions on Signal Processing.

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

[12]  Daniel W. Bliss,et al.  Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[13]  I. Reed,et al.  Rapid Convergence Rate in Adaptive Arrays , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Zishu He,et al.  Thinned knowledge-aided STAP by exploiting structural covariance matrix , 2017 .

[15]  Moe Z. Win,et al.  Message Passing Algorithms for Scalable Multitarget Tracking , 2018, Proceedings of the IEEE.

[16]  Jianxin Wu,et al.  Improving EFA-STAP performance using persymmetric covariance matrix estimation , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[17]  P. Stoica,et al.  Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: a refresher , 2004, IEEE Signal Process. Mag..

[18]  Guillaume Ginolhac,et al.  New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[19]  R.C. DiPietro,et al.  Extended factored space-time processing for airborne radar systems , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[20]  William L. Melvin,et al.  Space-time adaptive radar performance in heterogeneous clutter , 2000, IEEE Trans. Aerosp. Electron. Syst..

[21]  Zishu He,et al.  Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP , 2018, IEEE Geoscience and Remote Sensing Letters.

[22]  Hongwei Liu,et al.  Three-dimensional reduced-dimension transformation for MIMO radar space-time adaptive processing , 2011, Signal Process..

[23]  Xiaofeng Ma,et al.  Robust adaptive monopulse algorithm based on main lobe constraints and subspace tracking , 2017, EURASIP J. Adv. Signal Process..

[24]  Huadong Meng,et al.  Registration-based compensation using sparse representation in conformal-array STAP , 2010, Signal Process..

[25]  Augusto Aubry,et al.  Exploiting multiple a priori spectral models for adaptive radar detection , 2014 .

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

[27]  Aliazam Abbasfar,et al.  Efficient two-dimensional compressive sensing in MIMO radar , 2017, EURASIP J. Adv. Signal Process..

[28]  J. S. Goldstein,et al.  Subspace selection for partially adaptive sensor array processing , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[29]  Alexander M. Haimovich,et al.  An eigenanalysis interference canceler , 1991, IEEE Trans. Signal Process..

[30]  Augusto Aubry,et al.  Median matrices and their application to radar training data selection , 2014 .

[31]  Yan Zhou,et al.  The post-Doppler adaptive processing method based on the spatial domain reconstruction , 2015, Signal Process..

[32]  Joohwan Chun,et al.  An Improved Algebraic Solution for Moving Target Localization in Noncoherent MIMO Radar Systems , 2016, IEEE Transactions on Signal Processing.

[33]  Xavier Mestre,et al.  Finite sample size effect on minimum variance beamformers: optimum diagonal loading factor for large arrays , 2006, IEEE Transactions on Signal Processing.

[34]  Xiang Li,et al.  Knowledge-aided STAP with sparse-recovery by exploiting spatio-temporal sparsity , 2016, IET Signal Process..

[35]  Hong Wang,et al.  On adaptive spatial-temporal processing for airborne surveillance radar systems , 1994 .

[36]  Wei Xing Zheng,et al.  Matrix-Group Algorithm via Improved Whitening Process for Extracting Statistically Independent Sources From Array Signals , 2007, IEEE Transactions on Signal Processing.

[37]  Lingjiang Kong,et al.  Robust constrained waveform design for MIMO radar with uncertain steering vectors , 2017, EURASIP J. Adv. Signal Process..

[38]  Philippe Forster,et al.  Low-rank filter and detector for multidimensional data based on an alternative unfolding HOSVD: application to polarimetric STAP , 2014, EURASIP J. Adv. Signal Process..

[39]  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).