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- W2758637415 abstract "In order to solve performance reduction of space-time adaptive processing caused by interfering targets mixing in clutter training samples, a robust training samples detection method based on prior knowledge and sparse recovery is proposed. Firstly, the object region in unit to be detected is locked. Then the sparse complete base is got by discretizing the whole Angle-Doppler plane. After that, hollow out the corresponding object region in sparse complete base to estimate the clutter covariance matrix. Finally, the method is combined with generalized inner product method. Simulation analyses demonstrate the test statistics of proposed method have excellent discrimination validity, and can drastically eliminate interfering targets, which improves the target detection performance of STAP." @default.
- W2758637415 created "2017-10-06" @default.
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- W2758637415 date "2017-08-01" @default.
- W2758637415 modified "2023-09-26" @default.
- W2758637415 title "A training sample detection method based on sparse recovery" @default.
- W2758637415 hasPublicationYear "2017" @default.
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