Clutter Nulling Performance of SMI in Amplitude Heterogeneous Clutter Environments

In order to investigate the clutter nulling performance of the sample matrix inversion (SMI) algorithm in amplitude heterogeneous environments, we derive an analytical expression for the average signal-to-interference plus noise ratio (SINR) loss based on random matrix theory. The results show that comparing with the case where the training data utilizes the homogeneous secondary samples (which are independent and identically distributed (IID) to the snapshot in the cell under test (CUT)), formulating the adaptive weight with the secondary samples which have stronger clutter power than that in CUT will increase the output SINR. Nevertheless, the achievable performance improvement might be quite limited. Conversely, selecting the secondary samples with weaker clutter power will degrade the performance.

[1]  Allan Steinhardt,et al.  Improved adaptive clutter cancellation through data-adaptive training , 1999 .

[2]  José R. Casar,et al.  CFAR detection for Weibull and other log-log-linear tail clutter distributions , 1997 .

[3]  J. W. Silverstein.  Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices , 1995 .

[4]  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.

[5]  Yuri I. Abramovich,et al.  Detection–Estimation of Very Close Emitters: Performance Breakdown, Ambiguity, and General Statistical Analysis of Maximum-Likelihood Estimation , 2010, IEEE Transactions on Signal Processing.

[6]  L.E. Brennan,et al.  Theory of Adaptive Radar , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Yingning Peng,et al.  Convergence Rate of LSMI in Amplitude Heterogeneous Clutter Environment , 2010, IEEE Signal Processing Letters.

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

[9]  M. A. Weiner.  Binary integration of fluctuating targets , 1991 .

[10]  A.O. Steinhardt,et al.  Cost-efficient training strategies for space-time adaptive processing algorithms , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[11]  D. Schleher,et al.  Radar Detection in Weibull Clutter , 1976, IEEE Transactions on Aerospace and Electronic Systems.

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

[13]  T.L. Frey,et al.  An approximation for the optimum binary integration threshold for Swerling II targets , 1996, IEEE Transactions on Aerospace and Electronic Systems.

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

[15]  Xavier Mestre,et al.  Generalized Consistent Estimation on Low-Rank Krylov Subspaces of Arbitrarily High Dimension , 2009, IEEE Transactions on Signal Processing.

[16]  Xavier Mestre,et al.  DOA Estimation in the Small-Sample Threshold Region , 2009 .

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