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Multimodel Shrinkage for Knowledge-Aided Space-Time Adaptive Processing

Space-time adaptive processing gets its adaptivity from estimating the second-order statistics of the clutter from secondary data. In practice, the amount of available secondary data may not be sufficient for the sample covariance estimate to be accurate. Existing techniques exploit assumed properties of the clutter covariance, such as rank, to reduce the need for secondary training data. More recently in radar, single-model shrinkage estimators have been shown to reduce the reliance on training data given that a good a priori model for the covariance is available. We extend shrinkage estimation to a regularized multimodel approach that incorporates inexact knowledge of a digital elevation map to reduce the need for large amounts of secondary data. Using both simulation and the KASSPER I dataset, performance of the proposed approach is compared to low-rank estimation and various single-model shrinkage estimation approaches. The proposed approach offers the practical ability to reliably estimate the clutter covariance from a number of training data less than the rank of the clutter covariance matrix.