CFAR target detection algorithm based on dual-censoring threshold in non-homogeneous environments
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摘要: 为了解决雷达检测算法在非均匀噪声环境下目标检测性能严重下降的问题，在分析实际回波杂波分布特性的基础上，提出了一种基于双剔除门限的恒虚警目标检测算法，通过双剔除门限将极大极小干扰信号从参考窗口中剔除，实时精确估计背景噪声功率.经过与各检测算法仿真对比，该算法在多目标干扰、遮挡和杂波边缘干扰等非均匀背景噪声环境下仍具有最优的检测性能和鲁棒性.结果表明，所提出的目标检测算法在非均匀噪声环境下具有良好的检测性能.Abstract: In order to solve the problem that the detection performance of the radar target detector decreases badly in non-homogeneous environments. Based on the actual echo clutter distribution, a dual-censoring threshold constant false alarm rate (DCT-CFAR) detector is proposed. Dual censoring threshold is used to remove the large and small unwanted samples and real-time accurate estimate the background noise power level. Compared with the simulation and analysis results of other detectors, the proposed detector has the best detection performance and stability in multi-interfering targets, masking effect, clutter edge and other non-homogenous environments. The results show that the proposed detector still has a good detection performance in non-homogeneous environments.
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图 2 当 $t_{cs} $ 取值为0.01时, 虚警率与剔除门限 $t_{cl} $ 的关系
Fig. 2 Probability of false alarm versus $t_{cl} $ when $t_{cs} $ =0.01
图 3 当 $t_{cl} $ 取值为0.001时, 虚警率与剔除门限 $t_{cs} $ 的关系
Fig. 3 Probability of false alarm versus $t_{cs} $ when $t_{cl} $ =0.001
图 5 DCT-CFAR检测算法干扰目标剔除概率、信噪比与干扰目标数量关系
Fig. 5 The relationship of DCT-CFAR target detection algorithms eliminate interference probability, signal to noise ratio and interference target quantity
图 6 各检测算法在均匀背景噪声下的检测概率对比
Fig. 6 Probability of detection comparison between detectors in homogenous environment
图 7 各检测算法在4个干扰目标环境下检测概率对比
Fig. 7 Probability of detection comparison between detectors in 4 interfering targets environment
图 8 各算法在6个干扰目标环境下检测概率对比
Fig. 8 Probability of detection comparison between detectors in 6 interfering targets environment
图 9 各算法在8个干扰目标环境下检测概率对比
Fig. 9 Probability of detection comparison between detectors in 8 interfering targets environment
图 10 DCT-CFAR算法在多干扰目标下的检测性能
Fig. 10 Detection performance of DCT-CFAR detector in multi-interfering targets environment
图 11 DCT-CFAR和ACCA-ODV-CFAR检测算法在多目标干扰环境下的检测性能对比
Fig. 11 $P_{fa} $ comparison between DCT-CFAR and ACCA-ODV-CFAR detector in four interfering targets environment
图 12 DCT-CFAR和ACCA-ODV-CFAR检测算法在杂波边缘干扰环境下的检测性能对比
Fig. 12 $P_{fa}$ comparison of DCT-CFAR and ACCA-ODV-CFAR detector in clutter edge situation
表 1 各检测算法时间和空间复杂度对比
Tab. 1 Time and space complexity comparison of detectors
检测算法 时间复杂度 $T(n)$ 空间复杂度 $S(n)$ CA-CFAR $O(n\times1.0)$ $O(n\times1.0)$ OS-CFAR $O(n\times1.3)$ $O(n\times1.5)$ ACCA-ODV $O(n\times1.4)$ $O(n\times1.5)$ DCT-CFAR $O(n\times1.4)$ $O(n\times1.5)$ 
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[1] 章建成, 苏涛, 吕倩.基于运动参数非搜索高速机动目标检测[J].电子与信息学报, 2016, 6:1460-1467. http://d.wanfangdata.com.cn/Periodical/dzkxxk201606020 [2] 简涛, 苏峰, 何有, 等.复合高斯杂波下距离扩展目标的自适应检测[J].电子学报, 2012(5):990-994. http://d.wanfangdata.com.cn/Periodical_dianzixb201205020.aspx [3] 刘红亮, 周生华, 刘宏伟, 等.一种航迹恒虚警的目标检测跟踪一体化算法[J].电子与信息学报, 2016(5):1072-1078. http://www.cqvip.com/QK/91130X/201605/668829586.html [4] 于洪波, 王国宏, 曹倩, 等.一种高脉冲重复频率雷达微弱目标检测跟踪方法[J].电子与信息学报, 2015(5):1097-1103. doi: 10.11999/JEIT140924 [5] SHTARKALEV B, MULGREW B. Multistatic moving target detection in unknown coloured Gaussian interference[J]. Signal Processing, 2015, 115:130-143. doi: 10.1016/j.sigpro.2015.04.001 [6] DU B, ZHANG L P. Target detection based on a dynamic subspace[J].Pattern Recognition, 2014, 47:344-358. doi: 10.1016/j.patcog.2013.07.005 [7] LEI S W, ZHAO Z Q, NIE Z P, et al. Adaptive polarimetric detection method for target inpartially homogeneous background[J]. Signal Processing, 2015, 106:301-311. doi: 10.1016/j.sigpro.2014.07.019 [8] HOU H L, PANG C S, GUO H L, et al. Study on high-speed and multi-target detection algorithm based on STFT and FRFT combination[J]. Optik, 2016, 127:713-717. doi: 10.1016/j.ijleo.2015.10.140 [9] ZAIMBASHI A. An adaptive CA-CFAR detector for interfering targets and clutter-edge situations[J]. Digital Signal Processing, 2014, 31:59-68. doi: 10.1016/j.dsp.2014.04.005 [10] MEZIANI H A, SOLTANI F. Optimum second threshold for the CFAR binary integrator in Pearson-distributed clutter[J]. Signal, Image and Video Processing, 2012(6):223-230. doi: 10.1007/s11760-010-0207-3.pdf [11] WEINBERG G V, KYPRIANOU R. Optimised binary integration with order statistic CFAR in Pareto distributed clutter[J]. Digital Signal Processing, 2015, 42:50-60. doi: 10.1016/j.dsp.2015.04.002 [12] BOUDEMAGH N, HAMMOUDI Z. Automatic censoring CFAR detector for heterogeneous environments[J]. AEU-Internationl Journal of Electronics and Communications, 2014, 68:1253-1260. doi: 10.1016/j.aeue.2014.07.006 [13] ZAIMBASHI A, NOROUZI Y. Automatic dual censoring CA-CFAR detector in non-homogenous environments[J]. Digital Signal Processing, 2008, 88:2611-2621. doi: 10.1016/j.sigpro.2008.04.016 [14] MEZIANI H A, SOLTANI F. Decentralized fuzzy CFAR detectors in homogeneous Pearson clutter background[J]. Signal Processing, 2011, 91:2530-2540. doi: 10.1016/j.sigpro.2011.05.006 [15] SMITH M E, VARSHNEY P K. Intelligent CFAR processor based on data variability[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(6):837-847. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=869503 [16] MATA-MOYA D, DEL-REY-MAESTRE N, PELÁEZ-SÁNCHEZ V M., et al. MLP-CFAR for improving co-herent radar detectors robustness in variable scenarios[J]. Expert Systems with Applications, 2015, 42(11):4878-4891. doi: 10.1016/j.eswa.2014.12.055 [17] ZHANG R L, SHENG W X, MA X F, et al. Constant false alarm rate detector based on the maximal reference cell[J]. Digital Signal Processing, 2013, 23:1974-1988. doi: 10.1016/j.dsp.2013.07.009 [18] FARROUKI A, BARKAT M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments[J], IEEE Proc Radar Sonar Navig, 2005, 152:43-51. doi: 10.1049/ip-rsn:20045006 [19] 关键, 张晓利, 简涛, 等.分布式目标的子空间双门限GLRT-CFAR检测[J].电子学报, 2012, 9:1759-1764. http://www.cnki.com.cn/Article/CJFDTotal-JCDZ201504019.htm [20] 陈建军, 黄孟俊, 赵宏钟, 等.相参雷达时频域CFAR检测门限获取方法研究[J].电子学报, 2013, 8:1634-1638. doi: 10.3969/j.issn.0372-2112.2013.08.029 [21] GURAKAN B, CANDAN C, CILOGLU T. CFAR processing with switching exponential smoothers for nonhomogeneous environments[J]. Digital Signal Processing, 2012, 22:407-416. doi: 10.1016/j.dsp.2012.01.007 [22] WEINBERG G V. Management of interference in Pareto CFAR processes using adaptive test cell analysis[J]. Signal Processing, 2014, 104:264-273. doi: 10.1016/j.sigpro.2014.04.025 -
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