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Robust CFAR Ship Detector Based on Bilateral-Trimmed-Statistics of Complex Ocean Scenes in SAR Imagery: A Closed-Form Solution
IEEE Transactions on Aerospace and Electronic Systems ( IF 4.4 ) Pub Date : 2021-01-11 , DOI: 10.1109/taes.2021.3050654
Jiaqiu Ai , Yuxiang Mao , Qiwu Luo , Mengdao Xing , Kai Jiang , Lu Jia , Xingming Yang

A robust constant false alarm rate (RCFAR) detector based on bilateral-trimmed-statistics (BTS-RCFAR) with a closed-form solution is proposed. BTS-RCFAR aims at improving the detection performance in complex ocean scenes such as the multiple-target environment, off-shore, oil-spilled ocean area, etc. In these circumstances, the clutter samples are often contaminated by the outliers. Consequently, the estimated parameters are biased, and the probability density function modeling of the clutter is not accurate. Detection performance deteriorates with either decrease of the detection rate or increase of the false alarm rate. Inspired by Sigma filter, BTS-RCFAR proposes a bilateral-thresholds-based strategy to automatically trim the samples in the local reference window, both the high-intensity and the low-intensity outliers are eliminated. Furthermore, the trimming depth is adaptively derived according to the homogeneity of the clutter backgrounds, where the outliers are completely removed and the real clutter samples can be greatly sustained. Maximum-likelihood-estimator with a closed-form solution is used for parameter estimation using the bilateral-trimmed samples, and log-normal model of the sea clutter can be accurately established. Finally, the test cell is detected given the specified probability of false alarm rate. BTS-RCFAR improves the detection performance in complex ocean scenes by elevating the detection rate and reducing the false alarm rate. Both simulated data and real data are used for validation.

中文翻译:

基于 SAR 图像中复杂海洋场景双边修剪统计的鲁棒 CFAR 船舶探测器:一种封闭形式的解决方案

提出了一种基于双边修整统计 (BTS-RCFAR) 的稳健恒定误报率 (RCFAR) 检测器,具有封闭形式的解决方案。BTS-RCFAR旨在提高在多目标环境、近海、溢油海域等复杂海洋场景中的检测性能。在这些情况下,杂波样本经常被异常值污染。因此,估计的参数有偏差,杂波的概率密度函数建模不准确。检测性能随着检测率的降低或误报率的增加而恶化。受Sigma滤波器的启发,BTS-RCFAR提出了一种基于双边阈值的策略来自动修剪局部参考窗口中的样本,同时消除高强度和低强度的异常值。此外,修剪深度是根据杂波背景的均匀性自适应推导出来的,其中异常值被完全去除,真实的杂波样本可以得到极大的维持。使用具有封闭形式解的最大似然估计器进行双边修整样本的参数估计,可以准确地建立海杂波的对数正态模型。最后,在给定误报率概率的情况下检测测试单元。BTS-RCFAR 通过提高检测率和降低误报率来提高复杂海洋场景中的检测性能。模拟数据和真实数据都用于验证。完全去除异常值并且可以极大地维持真实的杂波样本。使用具有封闭形式解的最大似然估计器进行双边修整样本的参数估计,可以准确地建立海杂波的对数正态模型。最后,在给定误报率概率的情况下检测测试单元。BTS-RCFAR 通过提高检测率和降低误报率来提高复杂海洋场景中的检测性能。模拟数据和真实数据都用于验证。完全去除异常值并且可以极大地维持真实的杂波样本。使用具有封闭形式解的最大似然估计器进行双边修整样本的参数估计,可以准确地建立海杂波的对数正态模型。最后,在给定误报率概率的情况下检测测试单元。BTS-RCFAR 通过提高检测率和降低误报率来提高复杂海洋场景中的检测性能。模拟数据和真实数据都用于验证。给定的误报率概率检测到测试单元。BTS-RCFAR 通过提高检测率和降低误报率来提高复杂海洋场景中的检测性能。模拟数据和真实数据都用于验证。给定的误报率概率检测到测试单元。BTS-RCFAR 通过提高检测率和降低误报率来提高复杂海洋场景中的检测性能。模拟数据和真实数据都用于验证。
更新日期:2021-01-11
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