电子学报 ›› 2012, Vol. 40 ›› Issue (5): 990-994.DOI: 10.3969/j.issn.0372-2112.2012.05.020

• 学术论文 • 上一篇    下一篇

复合高斯杂波下距离扩展目标的自适应检测

简涛1, 苏峰1, 何友1, 平殿发1, 顾雪峰2   

  1. 1. 海军航空工程学院信息融合技术研究所, 山东烟台 264001;2. 海军工程大学科研部, 湖北武汉 430033
  • 收稿日期:2011-06-14 修回日期:2011-11-16 出版日期:2012-05-25
    • 基金资助:
    • 国家自然科学基金 (No.61102166); 海军航空工程学院青年科研基金

Adaptive Range-Spread Target Detectors for Compound-Gaussian Clutter

JIAN Tao1, SU Feng1, HE You1, PING Dian-fa1, GU Xue-feng2   

  1. 1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China;2. Office of Research and Development, Naval University of Engineering, Wuhan, Hubei 430033, China
  • Received:2011-06-14 Revised:2011-11-16 Online:2012-05-25 Published:2012-05-25

摘要: 研究了复合高斯杂波下的距离扩展目标自适应检测问题.基于辅助数据,利用采样协方差矩阵(SCM)和迭代(RE)估计矩阵,建立了自适应检测器AMSDD和基于动态阈值的ADT检测器,并分析了检测器的恒虚警率(CFAR)特性.理论分析表明,基于SCM的检测器只能保持对杂波协方差矩阵结构的自适应特性;而基于RE的检测器能同时获得对杂波协方差矩阵结构和纹理分量的CFAR特性.性能分析表明,对于AMSDD和ADT来说,与采用SCM估计器相比,采用RE估计器能使检测器获得更好的CFAR特性和检测性能.另外,基于RE的ADT检测器在目标散射点个数失配时具有很好的鲁棒性.

关键词: 复合高斯, 自适应检测, 距离扩展目标, 采样协方差矩阵, 协方差矩阵估计

Abstract: Adaptive detection of range-spread target is addressed in compound-Gaussian clutter.Based on the secondary data,the adaptive modified scatterer density dependent (AMSDD) detector and the adaptive-dynamic-threshold (ADT) detector are designed,with the sample covariance matrix (SCM) or the recursive estimation (RE) matrix.The theoretical analyses of constant false alarm rate (CFAR) show that,the SCM-based detectors only hold the adaptiveness to the clutter covariance matrix structure (CCMS),while the RE-based detectors can ensure CFAR to both of CCMS and texture components.The performance assessment shows that,as for AMSDD and ADT,the RE-based detectors outperform the SCM-based ones,in terms of CFAR properties and detection performance.Moreover,the ADT based on RE performs robustly in the mismatch cases of the expected number of scatterers.

Key words: compound-Gaussian, adaptive detection, range-spread target, sample covariance matrix, covariance matrix estimate

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