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

LI Zhi-hui;ZHANG Yong-shun;LIU Han-wei;WANG Qiang;LIU Yang

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2018, Vol. 39 ›› Issue (2) : 331-337. DOI: 10.3969/j.issn.1000-1093.2018.02.016
Paper

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

  • LI Zhi-hui1, ZHANG Yong-shun1,2, LIU Han-wei1, WANG Qiang1, LIU Yang1
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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 representationcoefficient 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 generalizedinner 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

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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. Acta Armamentarii. 2018, 39(2): 331-337 https://doi.org/10.3969/j.issn.1000-1093.2018.02.016

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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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