基于小波变换的脉冲激光雷达回波信号去噪方法的研究
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摘要
激光雷达的应用十分广泛,在军事和民用上都具有极高的应用价值,是目前高科技领域研究的热点。然而在实际的应用中,雷达信号不可避免地受到各种噪声和干扰的影响。为了提高激光雷达的探测精度,有效地去除噪声是非常重要的。小波变换是近年来发展起来的一种优良的信号处理理论,因此本文的目的是应用小波变换对脉冲激光雷达回波信号进行去噪处理。
本文针对影响脉冲激光雷达系统的噪声特性进行了分析,建立了理想的发射和回波信号模型。首先,应用三种小波阈值去噪法对理想的回波信号进行去噪处理,通过仿真比较得出：强制阈值去噪法是最适合所选信号的小波阈值去噪方法。然后,在强制阈值去噪的基础上,提出了一种更好的处理脉冲激光雷达弱信号的方法,即强制阈值相关性去噪法。对于实时性要求不高的场合,提出了在脉冲积累的基础上应用强制阈值相关性去噪法,通过MATLAB仿真验证了此方案的可行性及优越性。对于不规则的脉冲激光雷达回波信号,在强制阈值相关性去噪法的基础上加入了自动增益控制系统,仿真结果表明：波形恢复的很好,信噪比也得到了提高。
Laser radar has been extensively used both in military and civilian affairs. It is a focus in the high-tech fields at present. However in practical applications, the radar signal is inevitably influenced by various noises and interferences. Aiming to improve the detecting ability of radar, removing the noise effectively is very important. Wavelet transform is an excellent theory developing in recent years which can be used to signal processing. So the purpose of this thesis is to use wavelet transform to remove the noise of the pulse laser radar echo.
The noise characteristics affecting pulse lidar system is analyzed, the model of ideal transmitting signal and the return signal are created in this paper. First, the ideal return signals are denoised using three threshold denoising method, it is concluded that the denoising method of mandatory threshold is more suitable for the selected signal by comparing the simulation results of three threshold denoising method. Subsequently, on this foundation, a new denoising approach called mandatory threshold and correlation denoising approach is proposed which is more appropriate for weak signal processing of pulse lidar system. The method of employing the mandatory threshold and correlation denoising approach after the accumulating pulse is put forwarded which is suitable for the occasions having no high real-time demand. MATLAB simulation demonstrates the method is feasible and superior. On the basis of the mandatory threshold and correlation denoising processing to be added the Automatic Gain Control system for the irregular received signals. The simulation results demonstrate that the method is effective in the restoration of signals and the improvement of SNR.
本文针对影响脉冲激光雷达系统的噪声特性进行了分析,建立了理想的发射和回波信号模型。首先,应用三种小波阈值去噪法对理想的回波信号进行去噪处理,通过仿真比较得出：强制阈值去噪法是最适合所选信号的小波阈值去噪方法。然后,在强制阈值去噪的基础上,提出了一种更好的处理脉冲激光雷达弱信号的方法,即强制阈值相关性去噪法。对于实时性要求不高的场合,提出了在脉冲积累的基础上应用强制阈值相关性去噪法,通过MATLAB仿真验证了此方案的可行性及优越性。对于不规则的脉冲激光雷达回波信号,在强制阈值相关性去噪法的基础上加入了自动增益控制系统,仿真结果表明：波形恢复的很好,信噪比也得到了提高。
Laser radar has been extensively used both in military and civilian affairs. It is a focus in the high-tech fields at present. However in practical applications, the radar signal is inevitably influenced by various noises and interferences. Aiming to improve the detecting ability of radar, removing the noise effectively is very important. Wavelet transform is an excellent theory developing in recent years which can be used to signal processing. So the purpose of this thesis is to use wavelet transform to remove the noise of the pulse laser radar echo.
The noise characteristics affecting pulse lidar system is analyzed, the model of ideal transmitting signal and the return signal are created in this paper. First, the ideal return signals are denoised using three threshold denoising method, it is concluded that the denoising method of mandatory threshold is more suitable for the selected signal by comparing the simulation results of three threshold denoising method. Subsequently, on this foundation, a new denoising approach called mandatory threshold and correlation denoising approach is proposed which is more appropriate for weak signal processing of pulse lidar system. The method of employing the mandatory threshold and correlation denoising approach after the accumulating pulse is put forwarded which is suitable for the occasions having no high real-time demand. MATLAB simulation demonstrates the method is feasible and superior. On the basis of the mandatory threshold and correlation denoising processing to be added the Automatic Gain Control system for the irregular received signals. The simulation results demonstrate that the method is effective in the restoration of signals and the improvement of SNR.
引文
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[2]安毓英,曾晓东.光电探测原理[M].西安：西安电子科技大学出版社,2004.
[3]李建.水下图像后向散射噪声的去噪问题研究[D].硕士论文,中国海洋大学,2009.
[4]Rioul O. and Vetterli M. Wavelets and signal Processing [J]. IEEE Signal Processing Magzine,1991,8(4):14-38.
[5]Bahouxa,M., Rouat,J. Wavelet speech enhancement based on the teager energy operator[J].IEEE Signal Process.Lett.2001,8(1),10-12.
[6]I.Daubechies. Ten Lectures on Wavelets[M]. Society for Industrial and Applied Mathematics,Philadelphia,1992.
[7]Yao.J., Zhang,Y.T., The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations[J].IEEE Trans.Biomed.Eng,2002;49(11),1299-1309.
[8]Olivo J C., Automatic threshold selection using the wavelet transform[J]. CVGIP-Graph Models Image Process 1994,56(3):205-218.
[9]Mallat S., Zhong S., Characterization of signals from multiscale edges[J]. IEEE Tran Pattern Anal Mach Intell,1992,14(7):710-732.
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[11]简涛,何友等.小波变换在雷达信号检测中的应用[J].海军航空工程学院学报,2006,21(1)：121-126.
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[13]黄建,王新.小波闽值去噪的改、进方法[J].电子质量,2007,(9)：4-7.
[14]吴伟,蔡培升.基于MATLAB的小波去噪仿真[J].信息与电子工程,2008,6(3)：220-229.
[15]秦亚辉,冯景辉,陈立定.基于小波变换的信号去噪方法研究[J].信息技术,2010,(1)：53-57.
[16]张国华等.小波分析与应用基础[M].西安：西北工业出版社,2006.
[17]高成等. MATLAB小波分析与应用第2版[M].北京：国防工业出版社,2007.
[18]葛哲学,陈仲生MATLAB时频分析技术及其应用[M].北京：人民邮电出版社,2006.
[19]王沛,杨嘉林等.小波去噪理论的仿真研究[J].科技广场,2009,(9)：196-197.
[20]曾守桢.小波去噪及其在信号处理中的应用[D].硕士论文,天津大学,2007.
[21]郑钧,侯锐锋.小波去噪中小波基的选择[J].沈阳大学学报,2009,21(2)：108-110.
[22]王国恩,李静,厉春生.小波变换在信号去噪方面的应用研究[J].舰船电子工程,2008,(11)：80-83.
[23]Chang S G, Bin Yu, Vetterli M. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Trans Image Processing,2000,9(9): 1532-1546.
[24]Guohua Wei, Siliang Wu. Denoising radar signals using complex wavelet[A]//Signal Processing and Its Applications,2003. Proceedings. Seventh International Symposium on[C].2003,1:341-344.
[25]侯颖辉,许荣庆.星载雷达弱目标小波去噪方法研究[J].黑龙江大学自然科学学报,2006,23(5)：696-702.
[26]刘文涛,宋文爱.信号消噪的小波处理方法研究[J].弹箭与制导学报2008,28(2)：212-223.
[27]B.Albert. Wavelets,Gaussian mixtures and Wiener filtering [J]. Signal Processing, 2002, (82):709-712,.
[28]Bonneau R J, Wicks M C. Usiny(?) wavelet basis for a spectrally tunable phased array[A]//Radar Conference,2002. Proceedings of the IEEE[C].2002:110-112.
[29]Boisse J-C, Klaus V, Aubagnac J-P. A Wavelet Transform Technique for removing Airplane Echoes from ST Radar Signals[J]. Journal of Atmospheric and Oceanic Technology,1999, (16):334-346.
[30]Jin Min Kuo, Chen K-S. The application of wavelets correlator for ship wake detection in SAR images[J]. Geoscience and Remote Sensing, IEEE Transactions on,2003,41(6):1506-1511.
[31]Chen Guoying, Huang Peikang. Monopulse radar angle measurement error reduction based on wavelet multiresolution analysis[A]//Radar,1996. Proceedings, CIE International Conference of[C].1996:723-726.
[32]Liu Xiaoming, Suo Jidong, Liu Renjie, Wang Xin. The radar clutter processor with wavelet floating threshold[A]//Radar,2001 CIE International Conference on, Proceedings[C].2001:1001-1005.
[33]Naiwei Wang, Yingguang Zhang, Shunjun Wu. Radar waveform design and target detection using wavelets[A]//Radar,2001 CIE International Conference on, Proceedings[C].2001:506-509.
[34]Xiaoping Zhang, Mita D Desai. Segmentation of Bright Targets Using Wavelets and Adaptive Thresholding[J]. IEEE Transactions on Image Processing,2001, 10(7):1020-1030.
[35]周伟等MATLAB小波分析高级技术[M].西安：西安电子科技大学出版社,2006.
[36]章正宇,眭晓林.激光测距弱信号数字相关检测技术的研究和仿真[J].中国激光,2002,29(7)：661-665.
[37]肖洪梅,吴健,陈长庚,苏心智.微弱激光脉冲信号的相关检测[J].光学与光学技术,2004,2(1)：61-64.
[38]John J. Degnan, Jan F. MeGarry. SLR2000:Eyesafe and autonomous single photoelectron satellnelaser ranging at kilohertz rates[C]. SPIE,1997,3218:68-74.
[39]G. Y. Chen, T. D. Bui, A. Krzyzak. Image denoising using neighboring coefficients. IEEE Trans.On SP. Mar[J],2004,9(2):917-920.
[40]张静,柳晓鸣,索继东.用于雷达信号杂波抑制的小波算法选择[J].中国航海,2002,(3)：18-21.
[41]高志,余啸海. MATLAB小波分析与应用[M].北京：国防工业出版社,2007.
[42]曾国平.基于小波变换的图像去噪算法的研究[D].北京：北京交通大学,2007.
[43]马晓岩,李广柱,张贤达.基于小波变换的雷达信噪比改善分析[J].清华大学学报,2003,43(3)：422-424.
[44]丁鹭飞,耿富录.雷达原理[M].西安：西安电子科技大学出版社,2002.
[45]Antonini M., Barlaud M,Mathieu P., Image coding using wavelet transform[J]. IEEE Trans.on Image Processing,1992, (102):205-220.
[46]Yuanjin Zheng, B.H.T.David, and Lemin Li.Signal extraction and power spectrum estimation using wavelet transform scale space filtering and Bayes shrinkage[J]. Signal Processing,2000, (80):1535-1549.
[47]Kaiqi Huang, Zhenyang Wu, Color image denoising with wavelet thresholding based on human visual system model[C]. Proceedings of Visual Communication and Image Processing Conference(VCIP2003), Lugano, Switzerland, July 8-11, 2003, pp.5150;1667-1676.
[48]J.Modersitzki,Numerical Methods for Image Registration,Oxford University Press, New York,2004.
[49]M.I. Plett. Transient Detection With Cross Wavelet Transforms and Wavelet Coherence[J]. IEEE Transactions on Signal Processing.2007,55(5):1605-1611.
[50]罗万团,陈福深.改进的小波去噪法的研究[J].红外与激光工程.2008,37：226-228.
[51]司清亮.基于4种改进阈值函数的小波去噪[J].河南师范大学学报(自然科学版),2009,37(5)：140-142.
[52]Albert, C.To., Jeffrey, R.M.. Steven, D.G.. Wavelet denoising tech(?)ques with applications to experimental geophysical data[J]. Signal Processing.2009,(89): 144-160.
[53]刘正平,冯召勇,杨卫平.基于小波去噪的微弱信号提取[J].制造业自动化,2010,32(8)：98-101.
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