基于威布尔分布杂波模型的加权有序统计模糊CFAR检测算法
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摘要
为了解决有序统计恒虚警(order statistic constant false alarm rate,OS-CFAR)、有序统计最大选择恒虚警(order statistic greatest of-constant false alarm rate,OSGO-CFAR)和有序统计最小选择恒虚警(order statistic smallest ofconstant false alarm rate,OSSO-CFAR)检测算法在非均匀噪声环境下检测性能严重下降的问题,基于威布尔分布模型和模糊量化的软决策方法,提出了一种加权有序统计量的模糊恒虚警(weighted order statistic and fuzzy rules constant false alarm rate,WOSF-CFAR)检测算法。通过计算Leading和Lagging子窗口对应的模糊隶属函数值,采用代数积、代数和、最大选择和最小选择4种融合规则对2个子窗口的模糊输出量进行融合,并与比较门限进行比较判别目标有无。仿真表明,提出的检测方法与OSGO-CFAR,OSSO-CFAR算法相比,在均匀噪声、杂波边缘干扰和多目标干扰环境下均具有较好的检测性能,尤其是采用代数积融合规则时,检测性能最优,且提出的检测算法在均匀噪声环境下也具有最佳的检测性能。
In order to solve the problem that the detection performance of the conventional OS-CFAR,OSGO-CFAR and OSSO-CFAR detectors degrade severely in non-homogenous environment,based on Weibull distribution model and fuzzy soft decision method,a weighted order statistic and fuzzy constant false alarm rate( WOSF-CFAR) detection algorithm is proposed. The fuzzy membership function of Leading and Lagging sub-windows is calculated and the algebraic product,algebra sum,maximum and minimum fusion rule are used to fuse the fuzzy output of the two sub-windows and the comparison threshold to make decision is done. Through the simulation comparison,the WOSF-CFAR detection algorithm is superior to OSGO-CFAR and OSSO-CFAR algorithms in homogeneous and non-homogeneous environments. The results show that the proposed WOSF-CFAR detection algorithm not only provides low CFAR loss in homogenous environment but also performsrobustly in non-homogenous environments.
In order to solve the problem that the detection performance of the conventional OS-CFAR,OSGO-CFAR and OSSO-CFAR detectors degrade severely in non-homogenous environment,based on Weibull distribution model and fuzzy soft decision method,a weighted order statistic and fuzzy constant false alarm rate( WOSF-CFAR) detection algorithm is proposed. The fuzzy membership function of Leading and Lagging sub-windows is calculated and the algebraic product,algebra sum,maximum and minimum fusion rule are used to fuse the fuzzy output of the two sub-windows and the comparison threshold to make decision is done. Through the simulation comparison,the WOSF-CFAR detection algorithm is superior to OSGO-CFAR and OSSO-CFAR algorithms in homogeneous and non-homogeneous environments. The results show that the proposed WOSF-CFAR detection algorithm not only provides low CFAR loss in homogenous environment but also performsrobustly in non-homogenous environments.
引文
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[2]袁德诚,王运锋.杂波环境下雷达新建航迹处理方法分析[J].四川大学学报:自然科学版,2017,54:89-94.YUAN Decheng,WANG Yunfeng.Analyze Radar New Track Processing Methods in Clutter Environment[J].Journal of Sichuan University:Natural Science Edition,2017,54:89-94.
[3]ZAIMBASHI A.An adaptive CA-CFAR detector for interfering targets and clutter-edge situations[J].Digital Signal Processing,2014,31(5):59-68.
[4]WEINBERG G V,KYPRIANOU R.Optimized binary integration with order statistic CFAR in Pareto distributed clutter[J].Digital Signal Processing,2015,42(C):50-60.
[5]高振斌,田晓旭.基于FPGA的微弱信号检测与实现技术[J].重庆邮电大学学报:自然科学版,2016,28(3):297-302.GAO Z B,TIAN X X.Weak signal monitoring algorithm and realization technology on the FPGA[J].Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2016,28(3):297-302.
[6]CONTE E,MAIO A D,GALDI C.Statistical analysis of real clutter at different range resolutions[J].IEEE Trans Aerospace Electron Syst,2004,40(3):903-918.
[7]RAVID R,LEVANON N.Maximum likelihood CFAR for Weibull background[J].IEE Proc F,1992,139(3):256-264.
[8]DAVID M M,NEREA D R M,VICTOR M P S,et al.MLP-CFAR for improving coherent radar detectors robustness in variable scenarios[J].Expert Systems with Applications,2015,42(11):4878-4891.
[9]ELIAS A R,GARCIA G M.Analysis of some modified ordered statistic CFAR,OSGO and OSSO CFAR,IEEETrans Aerospace Electron Syst,1990,26(1):197-202.
[10]DU B,ZHANG L P.Target detection based on a dynamic subspace[J].Pattern Recognition,2014,47(1):344-358.
[11]MEZIANI H A,SOLTANI F.Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter[J].Signal Processing,2006,86(8):2115-2136.
[12]SHTARKALEV B,MULGREW B.Multistatic moving target detection in unknown coloured Gaussian interference[J].Signal Processing,2015,115(C):130-143.
[13]LEI S,ZHAO Z,NIE Z,LIU Q.Adaptive polarimetric detection method for target inpartially homogeneous background[J].Signal Processing,2015,106(C):301-311.
[14]LEYANON N,SHOR M.Order statistics CFAR for Weibull background[J].IEE Proc F,1990,137(3):157-162.
[15]VITO A DI,GALATI G,MURA R.Analysis and comparison of two order statistics CFAR systems[J].IEE Proc F,1994,141(2):109-115.