基于切割聚类的快速多分量LFM信号分离
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摘要
针对多分量线性调频(linear frequency modulation,LFM)雷达信号检测和参数估计精度低、计算速度慢等问题,提出了一种基于小波变换的切割聚类拟合参数估计的算法。该方法首先通过小波变换得到信号的三维时频分布图,其次采用等高线截取提取出小波脊线,再找出脊线的交点,以交点为界对小波脊线图进行切割,利用模糊C均值聚类完成各LFM分量脊线的聚类,最后分别对每段脊线进行拟合加权,从而估计出多分量LFM信号参数。仿真结果表明,与基于Hough变换检测直线方法相比,不仅在计算复杂度以及参数估计的准确度上都有较大的提升,而且当LFM信号分量达到4个以上亦有较准确的检测精度。
To solve the problems of low precision and slow computation speed in the multi-component linear frequency modulation(LFM)radar signals detection and parameter estimation,a novel parameter estimation algorithm with segmented clustering and fitting based on wavelet transform is proposed.Firstly,the 3Dtime-frequency distribution image of the signals is obtained by the wavelet transform,and extracts the wavelet ridge of the signals with the contour interception algorithm.Then find the intersection of the ridge,and cut the image of the wavelet ridge based on the intersection.After that,all the ridges of the LFM signals are clustered by FCM algorithm.Finally,each segment is fitted and weighted respectively and the parameters of the multi-component LFM signals are estimated.Simulation results show that the computational complexity and the precision of the parameter estimation notonly have greatly improved,but also have more accurate precision under the condition that LFM components reach more than four when compared with the traditional Hough transform for the line detection.
To solve the problems of low precision and slow computation speed in the multi-component linear frequency modulation(LFM)radar signals detection and parameter estimation,a novel parameter estimation algorithm with segmented clustering and fitting based on wavelet transform is proposed.Firstly,the 3Dtime-frequency distribution image of the signals is obtained by the wavelet transform,and extracts the wavelet ridge of the signals with the contour interception algorithm.Then find the intersection of the ridge,and cut the image of the wavelet ridge based on the intersection.After that,all the ridges of the LFM signals are clustered by FCM algorithm.Finally,each segment is fitted and weighted respectively and the parameters of the multi-component LFM signals are estimated.Simulation results show that the computational complexity and the precision of the parameter estimation notonly have greatly improved,but also have more accurate precision under the condition that LFM components reach more than four when compared with the traditional Hough transform for the line detection.
引文
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[14]Qiang G,Ya J L,Chang H W,et al.Novel detection method for multi-component LFM signals[C]∥Proc.of the Pervasive Computing Signal Processing and Applications,2010:759-762.
[15]Krinidis S,Chatzis V.A robust fuzz local information C-means clustering algorithm[J].IEEE Trans.on,Image Processing,2010,19(5):1328-1337.
[2]Bian H L.The research of some problems in nonstationary signals time-frequency joint analysis methods and application[D].Chengdu:University of Electronic Science and Technology,2008.(边海龙.非平稳信号联合时频分析方法的若干问题研究与应用[D].成都:电子科技大学,2008.)
[3]Yin X Z,Yu S L.Time-frequency analysis theory and its application[J].Modern Electronic Technique,2006,29(21):118-120.(殷晓中,于盛林.信号的时频分析理论与应用评述[J].现代电子技术,2006,29(21):118-120.)
[4]Yao J J,Yang L M,Feng J G.Application of common time-frequency analysis methods in analyzing characteristic quantity of digital seismic wave[J].Journal of Northwestern Seismological,2011,33(2):105-110.(姚家骏,杨立明,冯建刚.常用时频分析方法在数字地震波特征量分析中的应用[J].西北地震学报,2011,33(2):105-110.)
[5]Huang Y,Liu F,Wang Z Z,et al.Periodic FRFT-based multicomponent LFMCW radar signal separating[J].Acta Aeronautica et Astronautica Sinica,2013,34(4):846-854.(黄宇,刘锋,王泽众,等.基于周期FRFT的多分量LFMCW雷达信号分离[J].航空学报,2013,34(4):846-854.)
[6]Zhang L M,Zhong Z G,Wang Z Z,et al.Detection and separation of overlapped quasi-LFMCW signals based on periodic WHT recurrent filter[J].Acta Aeronautica et Astronautica Sinica,2013,34(11):2580-2589.(张立民,钟兆根,王泽众,等.基于周期WHT循环滤波的交叠类似LFMCW信号检测与分离[J].航空学报,2013,34(11):2580-2589.)
[7]Le B,Liu Z,Gu T.Weak LFM signal dectection based on wavelet transform modulus maxima denoising and other techniques[J].International Journal of Wavelets,Multiresolution and Information Processing,2010,8(2):313-326.
[8]Li Q,Wang Q.Wavelet spectrum and its application to LFM signal detection[J].Chinese Journal of Quantum Electronics,2005,5:103-108.
[9]Li L,Si X C,Chai J F,et al.Parameters estimation for LFM radar signal based on reassigned wavelet-radon transform[J].Journal of Systems Engineering and Electronics,2009,31(1):74-77.(李利,司锡才,柴娟芳,等.基于重排小波-Radon变换的LFM雷达信号参数估计[J].系统工程与电子技术,2009,31(1):74-77.)
[10]Chung K L,Chang T C,Huang Y H.Comment on:extended hough transform for linear feature detection[J].Pattern Recognition,2009,42(7):1612-1614.
[11]Zeng J X,Wang B B,Chen Z L.Random Hough transform lines detection based on the distance constraint[J].Journal of Nan Chang Hang Kong University(Natural Sciences),2011,25(3):7-11.(曾接贤,王斌斌,陈志良.基于距离约束的随机Hough变换直线检测算法[J].南昌航空大学学报(自然科学版),2011,25(3):7-11.)
[12]Matas J,Galambos C,Kittler J.Robust detection of lines using the progressive probabilistic hough transform[J].Computer Vision and Image Understanding,2005,78(1):119-137.
[13]Zhang J X,Shen X L,Wang H,et al.Fast multi-line detection algorithm using randomized hough transform[J].Journal of Zhejiang University of Technology,2013,41(3):346-350.(张江鑫,沈小兰,王辉,等.快速随机Hough变换多直线检测算法[J].浙江工业大学学报,2013,41(3):346-350.)
[14]Qiang G,Ya J L,Chang H W,et al.Novel detection method for multi-component LFM signals[C]∥Proc.of the Pervasive Computing Signal Processing and Applications,2010:759-762.
[15]Krinidis S,Chatzis V.A robust fuzz local information C-means clustering algorithm[J].IEEE Trans.on,Image Processing,2010,19(5):1328-1337.
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