基于空域相关阈值滤波的雾气激光信号处理算法
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摘要
针对雾气环境下微弱激光信号特征提取困难,在研究小波空域相关滤波算法基础上,提出了小波空域相关阈值滤波方法.阐述了小波空域相关阈值滤波的思想和原理,特别研究了滤波阈值选取的方法,比较了空域相关阈值滤波与空域相关滤波的滤波效果.结果表明对雾气环境下的激光信号,空域相关阈值滤波具有最好的滤波效果.在不同雾气浓度和不同信号信噪比情况下的实验结果表明,空域相关阈值滤波能够在保持激光回波信号特征的前提下有效地抑制大部分噪声,更适于雾气环境下微弱激光回波信号处理和特征提取.
In order to overcome the difficulty of extracting the faint laser signal feature in foggy environment,a wavelet spatial correlation threshold filtering method was proposed based on the study of wavelet filtering algorithm.The idea and principle of the spatial correlation threshold filter were elaborated,and a method was studied particularly for filtering threshold selection.Comparing with spatial correlation filtering method,the new spatial correlation threshold filtering method shows better filtering effect for the faint laser signal in foggy environment.The experiments results in different fog density and different signal to noise ratio(SNR)show that,the spatial correlation threshold filtering can effectively rule out most of the noise and maintain weak laser signal characteristics.The spatial correlation threshold filtering method is more suitable for processing faint laser signal and feature extraction in fog media.
In order to overcome the difficulty of extracting the faint laser signal feature in foggy environment,a wavelet spatial correlation threshold filtering method was proposed based on the study of wavelet filtering algorithm.The idea and principle of the spatial correlation threshold filter were elaborated,and a method was studied particularly for filtering threshold selection.Comparing with spatial correlation filtering method,the new spatial correlation threshold filtering method shows better filtering effect for the faint laser signal in foggy environment.The experiments results in different fog density and different signal to noise ratio(SNR)show that,the spatial correlation threshold filtering can effectively rule out most of the noise and maintain weak laser signal characteristics.The spatial correlation threshold filtering method is more suitable for processing faint laser signal and feature extraction in fog media.
引文
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[2]Daubechies I.The wavelet transform:time-frequency localization and signal analysis[J].IEEE Trans.on IT,1990,36(5):961-1005.
[3]余英林,谢胜利,蔡汉添.信号处理新方法导论[M].北京:清华大学出版社,2004.Yu Yinglin,Xie Shengli,Cai Hantian.Introduction to new signal processing approach[M].Beijing:Tsinghua University Press,2004.(in Chinese)
[4]Mallat S G.Zhong S F.Characterization of signals from edges[J].IEEE Trans PAMI,1992,14(7):710-732.
[5]Mallat S G,Hwang W L.Singularity detection and processing with wavelet[J].IEEE Transactions on Information Theory,1992,38(2):617-643.
[6]Downie T R,Silverman B W.The discrete multiple wavelet transform and thresholding methods[J].IEEE Trans.Signal Processing,1998,46(9):2558-2561.
[7]Xu Yansun,Weaver J B,Healy D M,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Trans,1994,3(6):747-757.
[8]Donoho D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[9]姚国仲.雾天交通中激光回波信号滤波算法研究[D].北京:北京理工大学,2011.Yao Guozhong.Research on filtering algorithm of laser signalinfoggy traffic[D].Beijing:Beijing Institute of Technology,2011.(in Chinese)