Non-homogeneous Training Sample Detection Method Based on Sparse Recovery with Prior Information

doi:10.3969/j.issn.1000-1093.2018.02.016

Abstract

Abstract: For the degradation of target detection performance in space-time adaptive processing (STAP) due to non-homogeneous training samples contaminated by target-like signals, a non-homogeneous training sample detection method based on prior information and sparse recovery is proposed. The sparse representation coefficient of cell under test (CUT) is recovered using focal underdetermined system solver (FOCUSS). A “sparse filter” is constructed based on radar system parameters.The target and “pseudo point” signals are filtered out by “sparse filter”, and the clutter covariance matrix is estimated. The generalized inner product (GIP) method is integrated to eliminate the contaminated training samples. Simulation analyses show that the proposed method can effectively eliminate the contaminated training samples and improve the target detection performance of STAP in non-homogeneous environment. Key

Key words: airborneradar, space-timeadaptiveprocessing, priorinformation, sparserecovery, non-homogeneoustrainingsampledetection

CLC Number:

TN957.51⁺3

LI Zhi-hui, ZHANG Yong-shun, LIU Han-wei, WANG Qiang, LIU Yang. Non-homogeneous Training Sample Detection Method Based on Sparse Recovery with Prior Information[J]. Acta Armamentarii, 2018, 39(2): 331-337.

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第39卷第2期
2018 年2月兵工学报ACTA
ARMAMENTARIIVol.39No.2Feb. 2018

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