文章摘要
卢术平,胡鹏,丁烽.复杂非均匀背景下的鲁棒声呐恒虚警检测算法[J].声学技术,2020,39(6):744~751
复杂非均匀背景下的鲁棒声呐恒虚警检测算法
Robust sonar CFAR detection algorithm in complex non-homogeneous background
投稿时间:2019-07-20  修订日期:2019-09-10
DOI:10.16300/j.cnki.1000-3630.2020.06.015
中文关键词: 非均匀背景  恒虚警检测  基于背景统计的恒虚警  鲁棒性
英文关键词: nonuniform background  constant false alarm ratio (CFAR) detection  background statistical characteristics based robust sonar target constant false alarm ratio (BSCR-CFAR)  robustness
基金项目:
作者单位E-mail
卢术平 杭州应用声学研究所, 浙江杭州 310023 lukeuestc@163.com 
胡鹏 杭州应用声学研究所, 浙江杭州 310023  
丁烽 杭州应用声学研究所, 浙江杭州 310023  
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中文摘要:
      针对复杂非均匀水下环境中目标检测问题,提出了一种基于背景统计特性的鲁棒声呐恒虚警(Background Statistical Characteristics based Robust Sonar Target Constant False Alarm Ratio,BSCR-CFAR)检测算法。该算法将自动删除平均级检测(Automatic Censored Mean Level Detection,ACMLD)和排序统计恒虚警(Order Statistic CFAR,OS-CFAR)检测算法引入可变指数恒虚警(Variability Index CFAR,VI-CFAR)检测算法中,并通过评估背景特性,自适应选择更匹配的CFAR检测方法。仿真和声呐实测数据分析结果表明,相比较单元平均恒虚警(Cell Average CFAR,CA-CFAR)、单元平均选大恒虚警(Greatest of CFAR,GO-CFAR)、单元平均选小恒虚警(Smallest of CFAR,SO-CFAR)和OS-CFAR、VI-CFAR等检测算法,该算法在混响边缘、混响区、单/多强离散干扰等典型非均匀背景下的恒虚警检测保持了良好的鲁棒性。
英文摘要:
      In this paper, a background statistical characteristics based robust sonar target constant false alarm ratio (BSCR-CFAR) detection algorithm is proposed to deal with the target detection problem in complex non-homogeneous underwater environment. In the proposed BSCR-CFAR detector, the automatic censored mean level detection (ACMLD) and order statistic CFAR (OS-CFAR) detection algorithms are applied to the variability index CFAR (VI-CFAR) detection algorithm, and then the more matched CFAR detection algorithms are adaptively selected by the assessment of background statistical characteristics. The simulation and the analysis results of sonar measured data indicate that by comparing with other detection algorithms, such as cell average CFAR (CA-CFAR), greatest of CFAR (GO-CFAR), smallest of CFAR (SO-CFAR), OS-CFAR and VI-CFAR, the performance of the proposed BSCR-CFAR method for CFAR detection still maintains better robustness in typical non-homogeneous environments, such as reverberation edge, reverberation region, and one or more strong discrete outliers.
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