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In this paper, we exploit the spiked covariance structure of the clutter plusnoise covariance matrix for radar signal processing. Using state-of-the-arttechniques high dimensional statistics, we propose a nonlinear shrinkage-basedrotation invariant spiked covariance matrix estimator. We state the convergenceof the estimated spiked eigenvalues. We use a dataset generated from thehigh-fidelity, site-specific physics-based radar simulation software RFView tocompare the proposed algorithm against the existing Rank Constrained MaximumLikelihood (RCML)-Expected Likelihood (EL) covariance estimation algorithm. Wedemonstrate that the computation time for the estimation by the proposedalgorithm is less than the RCML-EL algorithm with identical Signal to Clutterplus Noise (SCNR) performance. We show that the proposed algorithm and theRCML-EL-based algorithm share the same optimization problem in high dimensions.We use Low-Rank Adaptive Normalized Matched Filter (LR-ANMF) detector tocompute the detection probabilities for different false alarm probabilitiesover a range of target SNR. We present preliminary results which demonstratethe robustness of the detector against contaminating clutter discretes usingthe Challenge Dataset from RFView. Finally, we empirically show that theminimum variance distortionless beamformer (MVDR) error variance for theproposed algorithm is identical to the error variance resulting from the truecovariance matrix.

Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach