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Knowledge-Aided Space-Time Adaptive Processing

A fundamental issue in knowledge-aided space-time adaptive processing (KA-STAP) is to determine the degree of accuracy of the a priori knowledge and the optimal emphasis that should be placed on it. In KA-STAP, the a priori knowledge consists usually of an initial guess of the clutter covariance matrix. This can be obtained either by previous radar probings or by a map-based study. In this paper, we consider a linear combination of the a priori clutter covariance matrix with the sample covariance matrix obtained from secondary data, and derive an optimal weighting factor on the a priori knowledge by a two-step maximum likelihood (ML) approach. The performance of the two-step ML approach is compared with that of the convex combination (CC) approach and is evaluated using the KASSPER data.