Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations
Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations
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DOI：
10.1109/tgrs.2015.2451311
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发表时间：
2016-01-01
影响因子：
8.2
通讯作者：
Brekke, Camilla
中科院分区：
文献类型：
--
作者：
Tao, Ding;Anfinsen, Stian Normann;Brekke, Camilla
针对单视和多视强度合成孔径雷达数据中的舰船检测问题，提出了一种新的基于截断统计量（TS）的稳健恒虚警（CFAR）检测器。该方法是针对高目标密度的情况下，如忙碌航线和拥挤的港口，其中的背景统计估计从潜在的污染海杂波样本。CFAR检测器使用截断来排除可能的统计干扰离群值和TS，以对剩余的背景样本进行建模。推导出的截断统计恒虚警（TS-CFAR）算法不需要干扰目标的先验知识。TS-CFAR检测器提供准确的背景杂波建模，稳定的虚警调节特性，并在高目标密度情况下改善检测性能。
A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations.