【摘 要】
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This paper deals with subspace detection for range-spread target in non-homogeneous clutter with unknown covari-ance matrix where structured interference is presented in the re-ceived data.Through exploiting the persymmetry of the clutter covariance matri
【机 构】
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Department of Electronic Engineering,Xidian University,Xi’an 710071,China;Institute of Acoustics,Chi
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This paper deals with subspace detection for range-spread target in non-homogeneous clutter with unknown covari-ance matrix where structured interference is presented in the re-ceived data.Through exploiting the persymmetry of the clutter covariance matrix,we propose two adaptive target detectors,which are referred to as persymmetric subspace Rao to sup-press interference and persymmetric subspace Wald to sup-press interference(“PS-Rao-I”and“PS-Wald-I”),respectively.The persymmetry-based design brings in the advantage of easy implementation for small training sample support.The signal flow analysis of the two detectors shows that the PS-Rao-I re-jects interference and integrates signals successively through separated matrix projection,while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique pro-jection.In addition,both detectors are shown to be constant false alarm rate detectors,significantly improving the detection performance with other competing detectors under the condi-tion of limited training.
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