Average SCR loss analysis for polarimetric STAP with Kronecker structured covariance matrix

The paper presents the average signal-to-clutter loss (SCRL) analysis for polarimetric space-time adaptive processing by exploiting the Kronecker structure of the clutter covariance matrix (CM). An expression for the average SCRL as a function of the mean square error of the corresponding CM estimator is derived. Based on that expression, one can determine how many samples are required in order to achieve a desired SCRL. The proposed average SCRL analysis methodology can be extended to more general scenarios, where closedform CM estimates are not available. Simulations indicate that even in the non-asymptotic regime, the proposed method can provide a good prediction of the average SCRL.

[1]  D. Giuli,et al.  Polarization diversity in radars , 1986, Proceedings of the IEEE.

[2]  Jian Wang,et al.  Maximum Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters , 2006, IEEE Transactions on Signal Processing.

[3]  Ami Wiesel,et al.  Geodesic Convexity and Covariance Estimation , 2012, IEEE Transactions on Signal Processing.

[4]  Petre Stoica,et al.  On Estimation of Covariance Matrices With Kronecker Product Structure , 2008, IEEE Transactions on Signal Processing.

[5]  Björn E. Ottersten,et al.  Covariance Matching Estimation Techniques for Array Signal Processing Applications , 1998, Digit. Signal Process..

[6]  Fulvio Gini,et al.  Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter , 2002, Signal Process..

[7]  Prabhu Babu,et al.  Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions , 2015, IEEE Transactions on Signal Processing.

[8]  Hong Wang,et al.  Polarization-space-time domain generalized likelihood ratio detection of radar targets , 1995, Signal Process..

[9]  Xiang-Gen Xia,et al.  Average SINR Calculation of a Persymmetric Sample Matrix Inversion Beamformer , 2016, IEEE Transactions on Signal Processing.

[10]  G. J. Owirka,et al.  Optimal polarimetric processing for enhanced target detection , 1991, NTC '91 - National Telesystems Conference Proceedings.

[11]  Alfred O. Hero,et al.  Lower bounds for parametric estimation with constraints , 1990, IEEE Trans. Inf. Theory.

[12]  Lihua Xie,et al.  Vandermonde Decomposition of Multilevel Toeplitz Matrices With Application to Multidimensional Super-Resolution , 2015, IEEE Transactions on Information Theory.

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

[14]  G. Alfano,et al.  Polarization diversity detection of distributed targets in compound-Gaussian clutter , 2004 .

[15]  Arye Nehorai,et al.  Target Estimation, Detection, and Tracking , 2009, IEEE Signal Processing Magazine.

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

[17]  Thomas L. Marzetta,et al.  Parameter estimation problems with singular information matrices , 2001, IEEE Trans. Signal Process..

[18]  Arye Nehorai,et al.  Polarimetric Detection of Targets in Heavy Inhomogeneous Clutter , 2008, IEEE Transactions on Signal Processing.

[19]  Jianyu Yang,et al.  Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter , 2012, Digit. Signal Process..

[20]  Athina P. Petropulu,et al.  Polarimetric Detection in Compound Gaussian Clutter With Kronecker Structured Covariance Matrix , 2017, IEEE Transactions on Signal Processing.