基于奇异值分解的雷达微小目标检测方法
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摘要
提出了一种强杂波环境下雷达微小目标的检测方法。该方法以奇异值分解理论为基础,利用奇异值一阶、二阶差分谱进行奇异值选择,通过奇异值逆变换将雷达回波信号分解成不同的成份,从而实现杂波抑制和小微目标凸现。试验表明:该方法能有效抑制杂波,平均提升信噪比7 dB左右。
A detection method of radar small target in a strong clutter environment is proposed. On the basis of singular value decomposition theory, the first order and second order difference spectrum of singular values is used to select the singular values in various combination. Radar echo signal is decomposed into different compositions by inverse singular value transformation, thus realizing the clutter suppression and small target highlights. The experiment shows that the method can effectively suppress the strong clutter and improve the signal to noise ratio 7 dB of small targets.
A detection method of radar small target in a strong clutter environment is proposed. On the basis of singular value decomposition theory, the first order and second order difference spectrum of singular values is used to select the singular values in various combination. Radar echo signal is decomposed into different compositions by inverse singular value transformation, thus realizing the clutter suppression and small target highlights. The experiment shows that the method can effectively suppress the strong clutter and improve the signal to noise ratio 7 dB of small targets.
引文
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[3]刘伊丽,张临杰,张晰,等.双参数CFAR舰船检测算法两种实现方法之分析与比较[J].中国海洋大学学报,2017,47(10):136-142.LIU Yi-li,ZHANG Lin-jie,ZHANG Xi,et al.Comparison between two implementations of two-parameters CFAR ship detection method[J].Periodical of Ocean University of China,2017,47(10):136-142.
[4]ZAIMBASHI A.An adaptive CA-CFAR detector for interfering targets and clutter-edge situations[J].Digital Signal Processing,2014,31(8):59-68.
[5]WEINBERG G V,KYPRIANOU R.Optimized binary integration with order statistic CFAR in Pareto distributed clutter[J].Digital Signal Processing,2015,42(7):50-60.
[6]BOUDEMAGH N,HAMMOUDI Z.Automatic censoring CFAR detector for heterogeneous environments[J].International Journal of Electronics and Communications,2014,68(12):1253-1260.
[7]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.
[8]ZHANG Bo,SUN Lan-xiang,Yu Hai-bin,et al.A method for improving wavelet threshold demoising in laser-induced breakdown spectroscopy[J].Spectrochimica Acta Part BAtomic Spectroscopy,2015,107:32-44.
[9]李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.LI Hong-yan,ZHOU Yun-long,TIAN Feng,et al.Wavelet-based vibration signal de-noising algorithm with a new adaptive threshold function[J].Chinese Journal of Scientific Instrument,2015,36(10):2200-2206.
[10]曹晓英,张智军,向建军.基于提升小波改进阈值的雷达信号去噪方法[J].计算机工程与应用,2012,48(14):143-147.CAO Xiao-ying,ZHANG Zhi-jun,XIANG Jian-jun.Method of radar signal de-noising based on lifting wavelet improved threshold[J].Computer Engineering and Applications,2012,48(14):143-147.
[11]PARTHA R,MAITRA A K,ARIJIT B.A new threshold function for de-noising partial discharge signal based on wavelet transform[C]//International Conference on Signal Processing,Image Processing and Pattern Recognition[ICSIPR].Colmbatore:IEEE,2013:185-189.
[12]袁开明,舒乃秋,孙云莲,等.基于阈值寻优法的小波去噪分析[J].武汉大学学报:工学版,2015,48(1):74-80.YUAN Kai-ming,SHU Nai-qiu,SUN Yun-lian,et al.Wavelet denoising based on threshold optimization method[J].Engineering Journal of Wuhan University,2015,48(1):74-80.
[13]WANG Jian-hui,XIAO Qian,JANG Yan,et al.An adaptive filter model based on wavelet transform.[C]//WRI Global Congress on Intelligent Systems.Xiamen:IEEE,2009:136-139.
[14]ELYASI I,ZARMCHI S.Elimination noise by adaptive wavelet threshold[J].Engineering and Technology,2009(56):462-466.
[15]JHA S K,YADAVA R D S.Denoising by singular value decomposition and its application to electronic nose data processing[J].Sensors Journal,2011,11(1):35-44.
[16]ZHAO Xue-zhi,YE Bang-yan.Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock[J].Mechanical Systems and Signal Processing,2011,25(5):1617-1631.