Performance analysis of two covariance matrix estimators in compound-Gaussian clutter

The authors present a thorough performance analysis of two covariance matrix estimators, the sample covariance matrix estimator (SCME) and the normalised SCME (NSCME), which are employed by adaptive radar detectors in Gaussian and compound-Gaussian clutter. Theoretical performance predictions are derived, compared with the modified Cramer-Rao lower bound and checked with real-life sea clutter data. The results of the analysis show that the NSCME has superior performance in compound-Gaussian clutter and its performance is insensitive to the clutter multivariate distribution within the range cell under test and to the shape of the clutter correlation among different range cells. Conversely, the performance of the SCME heavily depends on the clutter distribution and has a dramatic worsening in spiky non-Gaussian clutter.

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

[2]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

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

[4]  K. J. Sangston,et al.  Coherent detection of radar targets in a non-gaussian background , 1994 .

[5]  Muralidhar Rangaswamy,et al.  Multichannel detection for correlated non-Gaussian random processes based on innovations , 1995, IEEE Trans. Signal Process..

[6]  F. Gini,et al.  Detection problem in mixed clutter environment as a Gaussian problem by adaptive preprocessing , 1995 .

[7]  Donald D. Weiner,et al.  Non-Gaussian clutter modeling with generalized spherically invariant random vectors , 1996, IEEE Trans. Signal Process..

[8]  E. Conte,et al.  Adaptive matched filter detection in spherically invariant noise , 1996, IEEE Signal Processing Letters.

[9]  C. Richmond.  A note on non-Gaussian adaptive array detection and signal parameter estimation , 1996, IEEE Signal Processing Letters.

[10]  D. McLaughlin,et al.  Performance of the GLRT for adaptive vector subspace detection , 1996 .

[11]  Pierfrancesco Lombardo,et al.  Coherent radar detection against K-distributed clutter with partially correlated texture , 1996, Signal Process..

[12]  Fulvio Gini,et al.  High resolution sea clutter data: statistical analysis of recorded live data , 1997 .

[13]  F. Gini.  Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter , 1997 .

[14]  J. Michels.  Covariance matrix estimator performance in non-Gaussian clutter processes , 1997, Proceedings of the 1997 IEEE National Radar Conference.

[15]  M. Rangaswamy,et al.  A parametric multichannel detection algorithm for correlated non-Gaussian random processes , 1997, Proceedings of the 1997 IEEE National Radar Conference.

[16]  Fulvio Gini,et al.  A cumulant-based adaptive technique for coherent radar detection in a mixture of K-distributed clutter and Gaussian disturbance , 1997, IEEE Trans. Signal Process..

[17]  Pierfrancesco Lombardo,et al.  Note on "Optimum and mismatched detection against K-distributed plus Gaussian clutter" , 1998 .

[18]  Fulvio Gini,et al.  A radar application of a modified Cramer-Rao bound: parameter estimation in non-Gaussian clutter , 1998, IEEE Trans. Signal Process..

[19]  F. Gini,et al.  Suboptimum approach to adaptive coherent radar detection in compound-Gaussian clutter , 1999 .

[20]  K. J. Sangston,et al.  Structures for radar detection in compound Gaussian clutter , 1999 .

[21]  A. Farina,et al.  Structures for Optimal and Suboptimal Coherent radar Detection in Compound Gaussian Clutter , 1999 .

[22]  Fulvio Gini,et al.  Clairvoyant and adaptive signal detection in non-Gaussian clutter: a data-dependent threshold interpretation , 1999, IEEE Trans. Signal Process..