电子学报 ›› 2016, Vol. 44 ›› Issue (2): 319-326.DOI: 10.3969/j.issn.0372-2112.2016.02.011

• 学术论文 • 上一篇    下一篇

复杂海杂波背景下分数低阶匹配滤波检测方法

郑作虎, 王首勇   

  1. 空军预警学院重点实验室, 湖北武汉 430019
  • 收稿日期:2014-06-19 修回日期:2015-04-17 出版日期:2016-02-25 发布日期:2016-02-25
  • 作者简介:郑作虎 男,1986年生于山东潍坊.空军预警学院重点实验室博士研究生.研究方向为雷达信号处理.E-mail:zhengzuohu@yeah.net;王首勇 男,1956年生于河南滑县.空军预警学院重点实验室教授、博士生导师、中国电子学会高级会员.主要研究方向为现代信号处理、雷达信号处理.
  • 基金资助:

    国家自然科学基金(No.61179014,No.61302193)

Radar Target Detection Method of Fractional Lower Order Matched Filter in Complex Sea Clutter Background

ZHENG Zuo-hu, WANG Shou-yong   

  1. Key Research Lab, Wuhan Air Force Early Warning Academy, Wuhan, Hubei 430019, China
  • Received:2014-06-19 Revised:2015-04-17 Online:2016-02-25 Published:2016-02-25

摘要:

针对在复杂海杂波背景下,雷达目标检测中动目标检测(Moving Target Detection,MTD)技术的检测性能显著下降的问题,以及局部最优检测器(Locally Optimum Detector,LOD)仅适用于低信杂比背景下弱目标检测的问题,基于分数低阶统计量理论,提出了一种分数低阶匹配滤波检测方法.该方法通过幂变换抑制杂波的非高斯特性,通过应用杂波分数低阶协方差矩阵特征值分解的方法白化相关杂波,在此基础上应用匹配滤波进行目标积累,以提高信杂比.通过仿真和实测数据,对所提出方法的检测性能进行了验证,并且与MTD和LOD进行了比较.结果表明,本文所提出方法能较好地解决非高斯相关杂波背景下的目标检测问题,检测性能明显优于MTD和LOD方法.

关键词: 非高斯相关杂波, 幂变换, 分数低阶协方差, 杂波白化

Abstract:

The detection performance of the Moving Target Detection (MTD) method of the radar target descends badly in complex sea clutter background, Also, The Locally Optimum Detector only works well for the weak target in the low signal clutter ratio background, Therefore, a fractional lower order matched filter detection method is proposed, which is obtained based on the fractional lower order statistics.The proposed method suppresses the non-Gaussian clutter by the signed power and whitens the correlated clutter by decomposing the clutter fractional lower order covariance matrix, at last the matched filter is used to get higher signal clutter ratio.Simulations and real data results show that, the detection performance of the proposed method obviously outperforms the MTD and LOD method in non-Gaussian correlated clutter background.

Key words: non-Gaussian correlated clutter, signed power, fractional lower order covariance, clutter whitening

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