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- W2108458053 abstract "In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary ldquotarget-freerdquo 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." @default.
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- W2108458053 date "2008-06-01" @default.
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- W2108458053 title "On Using a priori Knowledge in Space-Time Adaptive Processing" @default.
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- W2108458053 doi "https://doi.org/10.1109/tsp.2007.914347" @default.
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