Automatic censoring switching-CFAR detector based on sorting
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摘要: 针对传统恒虚警(Constant False-Alarm Rate, CFAR)检测器在非均匀噪声环境下检测性能较差的问题, 本文提出了一种基于排序的自动剔除Switching-CFAR(Automatic Censoring Switching-CFAR Detector Based on Sorting, ACS-CFAR)检测器.选择参考窗中间单元为测试单元, 其余单元按照幅值升序排列, 根据两个分界点位置参数, 选择合适的参考单元集进行背景噪声功率估计以及结合参考单元数和目标恒虚警率计算相关系数, 得到最优检测门限.经过仿真对比, ACS-CFAR检测器在均匀噪声环境下检测率为98.73%, 接近于单元平均恒虚警(CA-CFAR)检测器; 在非均匀噪声环境下检测率为98.16%, 优于可变索引恒虚警(VI-CFAR)和自动删除平均恒虚警(ACCA-CFAR)检测器, 虚警率误差均控制在0.10%以内.结果表明, 本文提出的ACS-CFAR检测器在均匀噪声环境以及杂波和多目标干扰环境下均具有较好的检测性能.
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关键词:
- 目标检测 /
- 恒虚警 /
- 自动剔除 /
- Switching-CFAR /
- 非均匀噪声
Abstract: Because the conventional CFAR (Constant False-Alarm Rate) detectors have poor detection performance in non-homogeneous environments, an automatic censoring switching-CFAR detector based on sorting (ACS-CFAR) is proposed. The middle cell of the reference window acts as a cell under test; other cells are sorted into the ranked reference cells by ascending order according to their magnitudes. According to the location parameters of the two boundary points which can effectively discriminate between thermal noise, clutter edge or interferences plus thermal noise and interferences immersed in the clutter plus thermal noise region, the detection algorithm can effectively select a suitable cell set from the ranked reference cells to estimate the unknown background level. Combined with the number of the selecting reference cells and the desired probability of false alarm, the corresponding scaling factor can be calculated. Finally, the adaptive detection threshold will be obtained according to background noise level estimated value and the calculated scaling factor. The performances of the ACS-CFAR detector is simulated and evaluated in different simulation environments and compared to the performance of the CA-CFAR, VI-CFAR and ACCA-CFAR detectors, the detection probability of ACS-CFAR detector is up to 98.73%, 98.16% in homogeneous and non-homogeneous environments, respectively. The ACS-CFAR detector performs like the CA-CFAR detector in homogeneous environments and better than the VI-CFAR and ACCA-CFAR detector in non-homogeneous environments, false alarm rate errors are controlled within $\pm $ 0.10%. The simulation results show that the proposed ACS-CFAR detector has better detection performance in homogenous and the presence of interfering targets and clutter edge environments. -
图 2 各检测器在均匀背景噪声下的检测率对比
Fig. 2 Probability of detection comparison between detectors in homogenous environment
图 3 各检测器在杂波干扰环境下的检测率对比
Fig. 3 PD comparison of the detectors in clutter edge situation
图 4 各检测器在杂波干扰环境下的虚警率控制对比
Fig. 4 Pfa comparison of the detectors in clutter edge situation
图 5 ACS-CFAR检测算法在不同信噪比条件下的剔除概率
Fig. 5 Probability of censoring in multiple target situations for two values of SNR
图 6 多目标干扰环境下剔除概率与干扰目标数之间的关系
Fig. 6 Relationship between the Probability of censoring and interfering targets number in multi-interfering targets environment
图 7 算法在不同目标剔除概率下的剔除概率对比
Fig. 7 Probability of censoring in multiple target situations for two values of Pfa
图 8 各检测器在多目标干扰环境下的检测概率对比
Fig. 8 Detection performance comparison between detectors in multi-interfering targets environment
图 9 ACS-CFAR算法在杂波和多目标干扰环境下的剔除概率
Fig. 9 Probability of censoring in clutter edge and multi-interfering target environment
图 10 各检测器在多目标和杂波干扰环境下虚警率对比
Fig. 10 Pfa comparison between detectors in clutter edge and multi-interfering target environment
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