期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 56, 期 6, 页码 2598-2602出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2007.914347
关键词
convex combination; general linear combination; knowledge-aided; space-time adaptive processing
In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary target-free data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance.
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