基于先验信息稀疏恢复的非均匀样本检测方法

doi:10.3969/j.issn.1000-1093.2018.02.016

兵工学报 ›› 2018, Vol. 39 ›› Issue (2): 331-337.doi: 10.3969/j.issn.1000-1093.2018.02.016

基于先验信息稀疏恢复的非均匀样本检测方法

李志汇¹，张永顺^1,2，刘汉伟¹，王强¹，刘洋¹

(1.空军工程大学防空反导学院, 陕西西安 710051; 2.信息感知技术协同创新中心, 陕西西安 710077)

收稿日期:2017-06-29 修回日期:2017-06-29 上线日期:2018-04-04
作者简介:李志汇(1991—), 男, 博士研究生。E-mail: lizhihui_16@163.com
基金资助:
国家自然科学青年基金项目（61501501）

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

LI Zhi-hui¹, ZHANG Yong-shun^1,2, LIU Han-wei¹, WANG Qiang¹, LIU Yang¹

(1.Air and Missile Defense College, Air Force Engineering University, Xi'an 710051，Shaanxi, China;2.Collaborative Innovation Center of Information Sensing and Understanding, Xi'an 710077, Shaanxi, China)

Received:2017-06-29 Revised:2017-06-29 Online:2018-04-04

摘要/Abstract

摘要： 针对训练样本被干扰目标污染导致空时自适应处理（STAP）目标检测性能下降的问题，提出了一种基于先验信息稀疏恢复的非均匀样本检测方法。该方法首先采用欠定系统局灶算法恢复待检测单元的稀疏表示系数，然后利用机载雷达系统参数等先验信息离线设计“稀疏滤波器”，并采用其滤除稀疏表示系数中的目标及“伪点”的影响，进而估计杂波协方差矩阵，最后与广义内积（GIP）方法结合，根据新的检测统计量来剔除被污染的样本。仿真分析表明，与传统GIP方法相比，该方法能够有效地检测出被干扰目标污染的训练样本，提升了STAP在非均匀环境下的目标检测性能。

关键词: 机载雷达, 空时自适应处理, 先验信息, 稀疏恢复, 非均匀样本检测

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

中图分类号:

TN957.51⁺3

李志汇，张永顺，刘汉伟，王强，刘洋. 基于先验信息稀疏恢复的非均匀样本检测方法[J]. 兵工学报, 2018, 39(2): 331-337.

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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基于先验信息稀疏恢复的非均匀样本检测方法

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

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