积雪路面GPR探测信号处理与雪铲自动控制研究
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摘要
本文针对犁式除雪车雪铲避障自动控制问题,在分析、验证积雪路面障碍探地雷达(Ground Penetrating Radar,简称GPR)探测可行性的基础上,对GPR探测回波信号的实时滤噪、障碍的实时成像、障碍信号的实时提取及雪铲避障的自动控制问题进行研究,揭示了积雪路面的GPR回波信号特性及障碍信号特性,解决了犁式除雪车除雪作业的障碍探测与雪铲避障自动控制问题。本文的主要研究及相关结论包括:
1、在全面分析路面积雪物理特性、路面材料特性、积雪路面常见障碍的基础上,依据电磁波传播理论,提出以电磁波方法探测积雪路面障碍;经理论分析及实验验证,遵循电磁波传播规律的GPR探测技术应用于除雪车除雪作业的积雪路面探测及雪铲自动控制方面完全可行。
2、分析、研究GPR回波信号的一维实时分析、处理技术。应用小波理论的Mallat算法(多辩分析算法)分解和重构GPR回波信号;计算机仿真及相关实验结果验证了Daubechies小波对积雪路面GPR回波信号的一维滤噪处理具有有效性、实时性。
3、依据地震波运动学,采用Stolt偏移算法,实现GPR探测积雪路面障碍的实时合成孔径成像;应用小波理论的二元多辩分析算法,对障碍图像进行分解与重构,实现障碍图像的实时增强,提高了除雪车操作人员监控的可视性。
4、建立积雪路面障碍GPR探测信息正演模型,采用相关系数法由GPR回波信号中提取路面障碍信号作为雪铲避障的自动控制信号;分析计算GPR信号处理速度、雪铲避障动作速度与除雪车除雪作业速度,对三者的时间匹配特性进行研究;计算机仿真分析及相关实验结果,验证了自动控制信号与时间匹配的正确性。
5、对人工神经网络应用于路面积雪厚度探测进行探讨性研究,认为在认知的基础上,通过GPR回波信号,人工神经网络经大量学习过程,能够识别路面积雪厚度。
The snowfull in winter always brings direct serious influence to the road traffic in the north areas. Technology for cleaning out RSSI (road-surface’s snow cover and ice, abbr. RSSI) is developed to clean out RSSI in time and to reduce the influence of snowfull. Because the mechanical method has the marked virtues to clean out RSSI, Japan has investigated the plough snow-shovel for cleaning out RSSI since 1950s. Up to now, the various equipments to clean out snow have been studied in Japan, Canada, Ameica, Russia, and so on, which are obtained widespread application. The technique of the mechanical method has been studied since 1970s in our country and made mighty advances up to the present. Meanwhile it is limited by the conditions such as design means and the correlative leave of technology; there are a great difference between China and other developed countries. And the effectual detecting for SCRS(the snow cover road-surface, abbr. SCRS ) has not been actualized when the sowplough cleaning out snow cover. So the snow-shovel has not automatically avoided the road-surface’s obstacles. Thus the snowplough or the road-surface is harmed easily. For this reason, it is important to develop the special snowplough which is suit for our country roads, can effectively detect road-surface’s obstacles,can keep a lookout over road-surface’s obstacles and automatically avoid road-surface’s obstacles. This paper studys on GPR(Ground Penetrating Radar, abbr GPR) detecting technology for SCRS and the signal processing and the automatic controlling technology of the snowplough’s snow-shovel from the practical requirement.
Analyse and Choice of Detecting SCRS Method
The snow-shovel configuration of the snowplough and the detecting SCRS method and the automatic control method of the snow-shovel and the capabilities of the snowplough to clean out snow cover depend on at the first hand the physical characteristics of the road-surface’s snow cover and the physical characteristics of the the road-surface’s materials and the road-surface’s obstacles. The physical characteristics are different with the circumstances. For this reason, they are the base of the studying on snowplough configuration and its control system to systemically analyse the physical characteristics, and to master the properties of the road-surface’s materials, and to investigate the familiar road-surface’s obstacles. In the non-contact detecting methods, the feasibility of sound-wave detecting SCRS and the electromagnetic wave detecting method was discussed in this paper, and the electromagnetic wave detecting method was deemed fitting for work of the snoeplough cleaning out snow cover. In the electromagnetic wave detecting methods, GPR detecting equipment is small in the size, easy for carrying about and so on. Form the theory of the electromagnetic wave radiating and the experiments of GPR detecting the snow cover, GPR is used to detect SCRS. This paper analysed and processed GPR signals to show the synchronous images of SCRS’s obstacles and extract the control sings for avoiding the obstacles when the snowplough cleaning out snow cover.
Linear Analysing and Processing of GPR Signals
The reflected signals of GPR detecting SCRS contains various interferential signals from the working environment. The interferential signals are filtrated by analysing and processing GPR reflected signals to retain the usable signals. The analysing and processing technology of the signals must be real-time and reliably to satisfy detecting SCRS during the snowplough working. The analysing and processing methods of the numerical signal contain the Fourier Transform (FT) and the Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT) and the Short-Time Fourier Transform (STFT) and the Wigner-Ville Distribution (WVD) and the Wavelet Transform and so on. The characteristics of these methods were analysed in this paper. The analyse shows that the Wavelet Transform is more suitable too analyse and process GPR reflected signals than other methods. The Mallat algorithm of the Wavelet Transform from the multiresolution analysis is used to decompose and reconstruct GPR reflected signals, so the interferential signals was effectively filtrated at the same time. The Daubechies wavelet of the Mallat algorithm was used to analyse and manage GPR reflected signals, and its validity and realtime are analysed by the computer simulation analysis. The validity and realtime for analysing and processing GPR reflected signals was tested by the experiments of GPR detecting SCRS.
Planar Imaging of GPR Reflected Signals
From the reflected signals of GPR detecting, the planar image of SCRS’s obstacles is constructed for the visual during the snowplough operator to enhance the security of the snowplough. From the theory of the seismic wave radiating, the finite-differences time-domain algorithm and the Stolt migration algorithm and the Phase-Shift algorithm are the representative algorithm of the wave equation migration imaging algorithm. Their capabilitys of the operating speed and the wave speed adaptability and the full eliminating boundary influence and etcetera were discussed in the paper. The discussions shows that the Stolt migration imaging algorithm is suitable for processing the real-time imaging information. GPR synthetic aperture imaging algorithm from the Stolt migration imaging algorithm is able to process the real-time imaging of SCRS’s obstacles detected by GPR. But the image visualization is not satisfying. Based on the duality multiresolution analysis algorithm of the Wavelet Transform, GPR image from the Stolt migration imaging algorithm was decomposed and reconstructed. So the real-time image of SCRS’s obstacles detected by GPR was enhanced, and the visualization was enhanced also at the same time.
Extracting Automatic Control Signal of Snow-shovel Obstacle-Avoidance and Analysing of Speed Match
From the linear analysing and processing of the reflected GPR signals, the obstacles signals was extracted in time by establishing the information model and forward modeling and using the correlation coefficient method. The extracted obstacles signals was used to control the snow-shovel automatic obstacle-avoidance when the snowplough cleaning out snow cover. The validity of the extracted obstacles signal was tested by the computer simulation analysis and the correlative experiments. The speed of the snowplough cleaning out snow cover and the analysing speed of GPR reflected signals and the response times of the snow-shovel automatic obstacle-avoidance form the match characteristic,and the match characteristic was analysed in the paper. From the analysis of the match characteristic, the paper discussed the connection between the setting distance of GPR antennas and the speed of the snowplough cleaning out snow cover.
Study of Using Artificial Neural Network to Calculate Snow Cover Thickness
Although the snow-shovel automatic obstacle-avoidance was realized and the planar real-time image of SCRS’s obstacles was showed by GPR detecting technology of SCRS when the snowplough cleaning out snow cover, it is difficult to differentiate the stratum signal from GPR reflected signals because of the randomicity of physical characters of the snow cover and the material of the roads. In this paper, the artificial neural network was explored to differentiate the stratum signal from GPR reflected signals and to calculate the road-surface’s snow cover thickness from the plentiful learning and cognizing of the artificial neural network. So the accurate information for the control and watch would been provided to the snowplough when it was cleaning out the road-surface’s snow cover. The time delay point algorithm was adopted to calculate the thickness of the road-surface’snow cover. The capability of the artificial neural network to calculate the thickness of the road-surface’s snow cover was simulated by the computer analysis. The actual detection data of GPR detecting the road-surface’s snow cover was provided to the artificial neural network to calculate the snow cover thickness. Compared values of the snow cover thickness calculated by the artificial neural network with values of the snow cover thickness measured actually, it was found that the artificial neural network calculated values of the snow cover thickness is near the actually measured values of the snow cover thickness when the snow cover was enough thick, and the thickness error is great when the snow cover was thin. On the condition of the plentiful learning and cognizing, the artificial neural network can calculate the snow cover thickness by using GPR reflected signals from the academic analysis.
1、在全面分析路面积雪物理特性、路面材料特性、积雪路面常见障碍的基础上,依据电磁波传播理论,提出以电磁波方法探测积雪路面障碍;经理论分析及实验验证,遵循电磁波传播规律的GPR探测技术应用于除雪车除雪作业的积雪路面探测及雪铲自动控制方面完全可行。
2、分析、研究GPR回波信号的一维实时分析、处理技术。应用小波理论的Mallat算法(多辩分析算法)分解和重构GPR回波信号;计算机仿真及相关实验结果验证了Daubechies小波对积雪路面GPR回波信号的一维滤噪处理具有有效性、实时性。
3、依据地震波运动学,采用Stolt偏移算法,实现GPR探测积雪路面障碍的实时合成孔径成像;应用小波理论的二元多辩分析算法,对障碍图像进行分解与重构,实现障碍图像的实时增强,提高了除雪车操作人员监控的可视性。
4、建立积雪路面障碍GPR探测信息正演模型,采用相关系数法由GPR回波信号中提取路面障碍信号作为雪铲避障的自动控制信号;分析计算GPR信号处理速度、雪铲避障动作速度与除雪车除雪作业速度,对三者的时间匹配特性进行研究;计算机仿真分析及相关实验结果,验证了自动控制信号与时间匹配的正确性。
5、对人工神经网络应用于路面积雪厚度探测进行探讨性研究,认为在认知的基础上,通过GPR回波信号,人工神经网络经大量学习过程,能够识别路面积雪厚度。
The snowfull in winter always brings direct serious influence to the road traffic in the north areas. Technology for cleaning out RSSI (road-surface’s snow cover and ice, abbr. RSSI) is developed to clean out RSSI in time and to reduce the influence of snowfull. Because the mechanical method has the marked virtues to clean out RSSI, Japan has investigated the plough snow-shovel for cleaning out RSSI since 1950s. Up to now, the various equipments to clean out snow have been studied in Japan, Canada, Ameica, Russia, and so on, which are obtained widespread application. The technique of the mechanical method has been studied since 1970s in our country and made mighty advances up to the present. Meanwhile it is limited by the conditions such as design means and the correlative leave of technology; there are a great difference between China and other developed countries. And the effectual detecting for SCRS(the snow cover road-surface, abbr. SCRS ) has not been actualized when the sowplough cleaning out snow cover. So the snow-shovel has not automatically avoided the road-surface’s obstacles. Thus the snowplough or the road-surface is harmed easily. For this reason, it is important to develop the special snowplough which is suit for our country roads, can effectively detect road-surface’s obstacles,can keep a lookout over road-surface’s obstacles and automatically avoid road-surface’s obstacles. This paper studys on GPR(Ground Penetrating Radar, abbr GPR) detecting technology for SCRS and the signal processing and the automatic controlling technology of the snowplough’s snow-shovel from the practical requirement.
Analyse and Choice of Detecting SCRS Method
The snow-shovel configuration of the snowplough and the detecting SCRS method and the automatic control method of the snow-shovel and the capabilities of the snowplough to clean out snow cover depend on at the first hand the physical characteristics of the road-surface’s snow cover and the physical characteristics of the the road-surface’s materials and the road-surface’s obstacles. The physical characteristics are different with the circumstances. For this reason, they are the base of the studying on snowplough configuration and its control system to systemically analyse the physical characteristics, and to master the properties of the road-surface’s materials, and to investigate the familiar road-surface’s obstacles. In the non-contact detecting methods, the feasibility of sound-wave detecting SCRS and the electromagnetic wave detecting method was discussed in this paper, and the electromagnetic wave detecting method was deemed fitting for work of the snoeplough cleaning out snow cover. In the electromagnetic wave detecting methods, GPR detecting equipment is small in the size, easy for carrying about and so on. Form the theory of the electromagnetic wave radiating and the experiments of GPR detecting the snow cover, GPR is used to detect SCRS. This paper analysed and processed GPR signals to show the synchronous images of SCRS’s obstacles and extract the control sings for avoiding the obstacles when the snowplough cleaning out snow cover.
Linear Analysing and Processing of GPR Signals
The reflected signals of GPR detecting SCRS contains various interferential signals from the working environment. The interferential signals are filtrated by analysing and processing GPR reflected signals to retain the usable signals. The analysing and processing technology of the signals must be real-time and reliably to satisfy detecting SCRS during the snowplough working. The analysing and processing methods of the numerical signal contain the Fourier Transform (FT) and the Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT) and the Short-Time Fourier Transform (STFT) and the Wigner-Ville Distribution (WVD) and the Wavelet Transform and so on. The characteristics of these methods were analysed in this paper. The analyse shows that the Wavelet Transform is more suitable too analyse and process GPR reflected signals than other methods. The Mallat algorithm of the Wavelet Transform from the multiresolution analysis is used to decompose and reconstruct GPR reflected signals, so the interferential signals was effectively filtrated at the same time. The Daubechies wavelet of the Mallat algorithm was used to analyse and manage GPR reflected signals, and its validity and realtime are analysed by the computer simulation analysis. The validity and realtime for analysing and processing GPR reflected signals was tested by the experiments of GPR detecting SCRS.
Planar Imaging of GPR Reflected Signals
From the reflected signals of GPR detecting, the planar image of SCRS’s obstacles is constructed for the visual during the snowplough operator to enhance the security of the snowplough. From the theory of the seismic wave radiating, the finite-differences time-domain algorithm and the Stolt migration algorithm and the Phase-Shift algorithm are the representative algorithm of the wave equation migration imaging algorithm. Their capabilitys of the operating speed and the wave speed adaptability and the full eliminating boundary influence and etcetera were discussed in the paper. The discussions shows that the Stolt migration imaging algorithm is suitable for processing the real-time imaging information. GPR synthetic aperture imaging algorithm from the Stolt migration imaging algorithm is able to process the real-time imaging of SCRS’s obstacles detected by GPR. But the image visualization is not satisfying. Based on the duality multiresolution analysis algorithm of the Wavelet Transform, GPR image from the Stolt migration imaging algorithm was decomposed and reconstructed. So the real-time image of SCRS’s obstacles detected by GPR was enhanced, and the visualization was enhanced also at the same time.
Extracting Automatic Control Signal of Snow-shovel Obstacle-Avoidance and Analysing of Speed Match
From the linear analysing and processing of the reflected GPR signals, the obstacles signals was extracted in time by establishing the information model and forward modeling and using the correlation coefficient method. The extracted obstacles signals was used to control the snow-shovel automatic obstacle-avoidance when the snowplough cleaning out snow cover. The validity of the extracted obstacles signal was tested by the computer simulation analysis and the correlative experiments. The speed of the snowplough cleaning out snow cover and the analysing speed of GPR reflected signals and the response times of the snow-shovel automatic obstacle-avoidance form the match characteristic,and the match characteristic was analysed in the paper. From the analysis of the match characteristic, the paper discussed the connection between the setting distance of GPR antennas and the speed of the snowplough cleaning out snow cover.
Study of Using Artificial Neural Network to Calculate Snow Cover Thickness
Although the snow-shovel automatic obstacle-avoidance was realized and the planar real-time image of SCRS’s obstacles was showed by GPR detecting technology of SCRS when the snowplough cleaning out snow cover, it is difficult to differentiate the stratum signal from GPR reflected signals because of the randomicity of physical characters of the snow cover and the material of the roads. In this paper, the artificial neural network was explored to differentiate the stratum signal from GPR reflected signals and to calculate the road-surface’s snow cover thickness from the plentiful learning and cognizing of the artificial neural network. So the accurate information for the control and watch would been provided to the snowplough when it was cleaning out the road-surface’s snow cover. The time delay point algorithm was adopted to calculate the thickness of the road-surface’snow cover. The capability of the artificial neural network to calculate the thickness of the road-surface’s snow cover was simulated by the computer analysis. The actual detection data of GPR detecting the road-surface’s snow cover was provided to the artificial neural network to calculate the snow cover thickness. Compared values of the snow cover thickness calculated by the artificial neural network with values of the snow cover thickness measured actually, it was found that the artificial neural network calculated values of the snow cover thickness is near the actually measured values of the snow cover thickness when the snow cover was enough thick, and the thickness error is great when the snow cover was thin. On the condition of the plentiful learning and cognizing, the artificial neural network can calculate the snow cover thickness by using GPR reflected signals from the academic analysis.
引文
[1] 裴玉龙,孟祥海等.寒冷地区道路交通事故分布的研究.中国公路学报,1998(1):89～94
[2] 麻志宏.用仿生学原理解决清冰雪路面高低不平的难题.哈尔滨市交通工程学会四届三次会员代表大会会议文献.哈尔滨:哈尔滨市交通工程学会,2001
[3] 张长荣.道路除雪机的主要结构及其功能.建筑机械化,1999(3):14～15
[4] 李盛林,张宏文.公路快速清雪装置的设计.筑路机械与施工机械化,2002(1):3～4
[5] 张长荣.除雪机配用车辆的选择与应用.建筑机械化,2000(1):41～42
[6] 李盛林,孟祥拴等.国内外除雪装置.筑路机械与施工机械化,2003(1):17～19
[7] 陆季挺,靳长征.高速公路快速除雪作业.筑路机械与施工机械化,1999(2):43～45
[8] 赵恩棠.道路除雪融冰的措施.东北公路,1994(4):28～32
[9] 高存原,陆季挺.道路冰雪清除方法及其应用.山西交通科技,2000(2):52～54
[10] 韩爱民,国委文.CBX—1800 型除冰雪机.农业机械化与电气化,2002(1):37
[11] 杨光,李广鹏.CL3.6 型犁式除雪装置的开发研制.筑路机械与施工机械化,1998(3):10～11
[12] 吾布利.小型除雪机的研制.新疆农机化,2000(6):24
[13] 刘大志.北方高等级公路的除雪方法.筑路机械与施工机械化,1998(6):35～36
[14] 王承惠.永江清雪除冰专用车辆的技术性能和特点.专用汽车,2003(11):54
[15] 唐文初.旋转机械式路面冰雪装置.机械设计,1996(9):28～30
[16] 张长荣.振动压裂式破冰雪机的研制.筑路机械与施工机械化,1999(6):16～17
[17] 李乔非,吴书琴.除雪机除雪部件的实验研究.机械工程师,1995(2):12～13
[18] 王新梅,殷林.可调压式自动越障式除雪装置.工程机械,1999(10):10～11
[19] 马君,陈伟旭等.如何使用 QX—1.4 型清雪机.黑龙江水产,2002(5):23～24
[20] 朱彩霞,夏智武.QX700 型庭院清雪机的研制.新疆农机化,2002(5):31～32
[21] 刘延超,张春宇等.CBX—1500 型冰雪清除机的研制.黑龙江交通科技,2002(1):45～46
[22] 杨光,刘大志.CBX—1600 型除冰雪机的功率计算.筑路机械与施工机械化,1996(2):5～6
[23] 关明慧,徐宇工等.微波加热技术在清除道路积冰中的应用.北方交通大学学报,2003(4):79～83
[24] 马文星,邓洪超.筑路与养护路机械.北京:化学工业出版社,2005
[25] Steinfeld Aaron, Tan Han-Shue,Bougler Benedicte.Naturalistic findings for assisted snowplow operation.Proceedings of the Human Factors and Ergonomics Society,2001:1651～1655
[26] Da Cunha Manuel.County improves its snow and ice control program .Public Works,1996(4):28～29
[27] Olson Walter W., Kempainen, Alan, etc.Dynamic modeling of forces on snowplow equipped trucks.Heavy Vehicle and Highway Dynamics(SAE Special Publications, v 1308, Nov), 1997:49～55
[28] Arnold James A..New applications make NDGPS more pervasive.Public Roads, 2001(4):39～43
[29] Smithson Leland D..Smith, Duane E..Engineering cold regions maintenance equipment for the 21st century.Proceedings of the International Conference on Cold Regions Engineering, 1998: 672～683
[30] McGehee D.V. , Raby, M., Nourse, G.E..Defining the operator interface of a snowplow lane tracking system from field interviews and surveys.Transportation Research Record, 2000(n 1700):38～44
[31] Roosevelt D.S., Hanson, R.A., Campenni, W.M. .Automatic vehicle location system in urban winter maintenance operations.Transportation Research Record, 2001(n 1741): 6～10
[32] 崔宪江编译.除雪机械.北京:人民交通出版社,1988
[33] 余琴.C 波段多极化 SAR 反演积雪湿度模型研究[D].中国科学院遥感应用研究所, 2004
[34] 赵洪勇,吕宾林,朱忠林.声波测试法在铁路路基测试中的应用.铁道建筑,2005(12):45～46
[35] 佐藤和敏,彭惠民译.用超声波探测道口障碍物的道口安全新技术.中国铁路,2003(2):65～68
[36] 雷旭友.岩溶在地质雷达图像和ρs 断面图上的反映特征.中国地质灾害与防治学报,1997(8):143～154
[37] 张河水,陆曙明等.地质雷达在高速公路建设中的应用.公路,1998(8):40～42
[38] 赵楠,王惠芳.地质雷达在工程勘察中的应用.勘察科学技术,1998(3):58～63
[39] 岳建华,何兵涛.超吸收边界条件在地质雷达剖面正演中的应用,中国矿业大学学报,1999(5):453～456
[40] 李张明.地质雷达在三峡工程施工阶段应用研究.CT 理论与应用研究,2000(3):41～46
[41] 李大洪.影响地质雷达工作频率选择的若干因素.矿业安全与环保,2000(6):29～30
[42] 宋雷,黄家会等.电磁波速度层析成像技术用于探测地下深部岩层特性研究.工程勘察,2000(2):62～63
[43] 杨立新.雷达在检测路面厚度中的应用.湖南交通科技,2001(1):18～19
[44] 姬继法,潘纪顺.应用地质雷达探测小型不良地质体.地质与勘探,2002(1):80～82
[45] 陈爱云,方云.地质雷达在石质文物保护工程中的应用.西部探矿工程,2003(8):165～166
[46] 李成香,强建科,王建军.地质雷达在公路裂缝检测中的应用.工程地球物理学报,2004(4):282～286
[47] Annan A P.Design and development of a digital discrete measurement ground penetrating radar system.Paper presented at the working on ground penetrating radar sponsored by the Geological Survey of Canada, Ottawa, 1988
[48] James S Mellett.Ground penetrating radar applications in engineering ,environmental management, and geology[J] .Journal of Applied Geophysics, 1995(33):157～166
[49] Darid F Dominic.Delineation of shallow stratigraphy using ground penetrating radar[J] .Journal of Applied Geophysics, 1995(33):166～170
[50] Kovas A, Morey R M.Electromagnetic measurements of multi-year sea ice using impule radar.Gold Regions Science and Technology, 1986(12):67～93
[51] Kwok P K, Rignot E, Holt B, etc.Identification of sea ice types in spaceborne synthetic aperture radar data.Journal of Geophysical Research,1992(97C2):2391～2402
[52] 金亚秋,陈轶等.雷达卫星 SAR 与防卫气象卫星 SSM/I 对渤海海冰的观测研究.地球物理学报,2001(2):163～170
[53] 孙波,温家洪等.北冰洋冰厚度穿透雷达探测与下表层形态特征分析.中国科学(D 辑),2002(11)951～957
[54] 张宏祥,盛阳.公路地质雷达在检测公路路面厚度中的应用.森林工程,2001(3):37～38
[55] 杨天春,吕绍林,伍永贵.地质雷达检测道路结构的原理及应用分析.中南工业大学学报,2001(2):118～121
[56] 黎滨洪,金荣洪,张佩玉.电磁场与波.上海:上海交通大学出版社,1996
[57] 王秉中.计算电磁学.北京:科学出版社,2002
[58] 谢处方,饶克谨.电磁场与电磁学.北京:北京大学出版社,1987
[59] 曾绍发,刘四新等.探地雷达方法原理及应用.北京:科学出版社,2006
[60] 李大心.探地雷达原理与应用.北京:地质出版社,1994
[61] LTD—2000 探地雷达使用手册.青岛:中国电波传播研究所,2005.03
[62] 王朝英,冯新喜.信号处理原理.北京:清华大学出版社,北京交通大学出版社,2005
[63] 杨建国.小波分析及其工程应用.北京:机械工业出版社,2005
[64] 邹海波,宁书年等.小波理论在探地雷达信号处理中的作用.地球物理学进展,2004(2):268～274
[65] Alexander K Kerimov,Vladimir V Kopeikin.The inverse problem forGPR of impulse type via optional control theory.7th Intern. GPR conf. ,1998(1):309～312
[66] Lazaro-Macncilla O,Gomez-Trevinon E.One-Dimensinal GPR inverse problem.7th Intern. GPR conf. ,1998(1):258～289
[67] Zhixiong Wu,Ce Liu.An image reconstruction method using GPR data.IEEE Transon GPR,1999 (1) :327～334
[68] Admana Bitri,Gilles Grandjecon.Frequency-wave number modeling and migration of 2-D GPR data in moderate heterogeneous dispersive media.Geophysical Prospecting,1998 (3) :387～301
[69] 陈文超,师振盛等.小波变换在去除探地雷达信号直达波的作用.电波科学学报,2000(1):352～357
[70] 肖艳军,李建勋.基于小波变换的信号滤波在探地雷达中的作用.电波科学学报,2006(1):140～145
[71] 邹海林,隋亚莉等.基于小波变换的 GPR 图象去噪方法研究.系统仿真学报,2005(4):855～862
[72] 邓华,蔡迎春等.小波变换在路用雷达信号处理中的应用.郑州大学学报,2002(1):101～103
[73] 詹毅,李修忠等.用连续小波变换分析探地雷达回波信号.物探与化探,1999(6):443～447
[74] 詹毅,梁昌洪等.探地雷达回波信号处理中小波变换域方法研究.西安电子科技大学学报,1999(3):305～309
[75] 陈文超.小波变换应用于探地信号处理的研究[D].西安交通大学,西安:2000
[76] 刘明才.小波分析及其应用.北京:清华大学出版社,2005
[77] 梁学章,何甲兴等.小波分析.北京:国防工业出版社,2005
[78] 孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[79] 彭玉华.小波变换与工程应用.北京:科学出版社,1999
[80] 高静怀,王文秉等.地震资料处理中小波函数的选取研究.地球物理学报,1996(3):392～400
[81] D.J Daniels.Surface pentrating radar.Published by IEE,London,UK,1996
[82] Schneider W.A . Intergral formulation for migration in two and three dimansion.Geophysics,1978,Vol.(43):49～76
[83] Berkhout A.J.Steep dip finite-difference migration.Geophysical prospecting,1979,Vol.(27):196～213
[84] 马在田.地震成像技术.北京:石油出版社,1989
[85] Yu H.,Ying X.,Shi Y..The use of FK techniques in GPR processing.6’th International conference on ground penetrating rader(Japan),1996:595～600
[86] Zhang Anxue,Jiang Yansheng,etc..Experimental sudies on GPR velocity estimation and imaging method using migration in frequency-wavenumber domain.Proceedings ISAPE 2000 5’th international symposium,2000:468～473
[87] 王光力.探地雷达成像方法研究[D].天津:中国民用航空学院,2004
[88] 王光力,吴仁彪.探地雷达 phase-shift 偏移成像算法研究.第九届雷达年会论文集,2006:415～418
[89] 张春城,周正欧.基于 Stolt 偏移的探地雷达合成孔径成像研究.电波科学学报,2004(3):316～320
[90] 陈延梅,邓延权,曲文波.Stolt 45°上行波方程偏移的一种新算法.黑龙江大学自然科学学报,2002(1):21～26
[91] 刘喜武,刘洪.波动方程地震偏移成像方法的现状与进展.地球物理学进展,2002(4):583～589
[92] 孔令讲,周正欧.波速误差对浅地层探地雷达 SAR 算法影响的研究.电波科学学报,2005(6):819～823
[93] 毛宁波,戴塔根.地震成像与空间成像、医学成像的交叉与融合.地球物理学进展,2003(4):643～646
[94] 罗银河,刘江平,俞国柱.叠前深度偏移述评.物探与化探,2004(6):540～545
[95] 金胜汶,许士勇,吴如山.基于波动方程的广义屏叠前深度偏移.地球物理学报,2002(5):1～7
[96] 丁伟,李振春,刘玉莲.基于近似的联合叠前深度偏移方法.石油物探,2003(1):29～34
[97] 尹军杰,刘学伟等.基于散射成像数值模拟的地震采集参数论证.石油物探,2005(1):58～64
[98] 姚萌,刘树人,杨燕.基于探地雷达的古墓遗址探测及数据后处理方法.遥感学报,2001(4):317～320
[99] 刘振春,刘玉莲等.基于最优 Born 近似的叠前深度偏移方法.石油大学学报(自然科学版),2002(5):32～36
[100] 范立生,高明星等.极化 SAR 遥感中森林特征的提取.电波科学学报,2005(5):553～556
[101] 张钋,李幼铭,刘洪.几类叠前深度偏移方法的研究现状.地球物理学进展,2000(2):30～39
[102] 黄伟,邓世坤.偏移方法用于探地雷达图像处理的有效性研究.地质科技情报,2001(4):99～102
[103] 王棣,王华忠等.频率波数域共偏移距叠前时间偏移方法.石油物探,2004(1):8～10
[104] 周辉,王兆磊等.同时实现地质雷达数据地形校正和偏移成像的方法.吉林大学学报(地球科学版),2004(3):459～463
[105] 何樵登.地震勘探原理与方法.北京:地质出版社,1986
[106] 吴明赞,王志功.基于 ADSP 的小波图像增强算法实现.计算机应用研究,2006(6):160～164
[107] 郑慕之,陶玲,王惠南.MRI 图像的多尺度小波增强新算法.中国医疗器械信息,2004(4):9～11
[108] 曾鹏鑫,么健石等.基于小波变换的图像增强算法.东北大学学报(自然科学版),2005(6):527～530
[109] 张瑾,陈向东.小波分析在遥感图像增强中的应用及 MATLAB 实现.半导体光电,2005(3,增刊):149～151
[110] 赵选科,赵庆武,沈晓芳.基于联合变换相关的光电小波实时图像边缘增强.光电子技术与信息,2004(3):55～57
[111] 李郁,闫敬文.一种基于小波分析的 SAR 图像增强去噪方法.计算机工程与设计,2004(2):309～310
[112] Ji Shu-peng,Ding Xiao-qing.Morphological filters and wavelet-based histogram equalization image enhancement for weak target detection.Infrared technology,2003(4):32～38
[113] 成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004
[114] A. Giannopoulos.Modelling ground penetrating radar by GprMax.Construction and Building Materials,2005(19): 755～762
[115] Antonis Giannopoulos.GprMax2D/3D User's Manual Version 2.0.USA:2005
[116] 粟毅,黄春琳,雷文太.探地雷达理论与应用.北京:科学出版社,2006
[117] 吴超.基于路面探测信息的清雪车自动控制研究[D].长春:吉林大学,2006
[118] 高文森,张魁元等.概率统计教程.长春:东北师范大学出版社,1999
[119] 王栋梁等.非对称阀控制非对称缸的分析研究.山东建材学院学报,2001(6) :123～127
[120] 高钦和,黄先祥.基于 simulink 的重物举升液压控制系统建模与仿真.机床与液压, 2001(1):61～63
[121] 付永领,祁晓野. AMESim 系统建模和仿真—从入门到精通.北京:北京航空航天出版社,2006
[122] 飞思科技产品研发中心.神经网络理论与 MATLAB7 实现.北京:电子工业出版社,2005
[2] 麻志宏.用仿生学原理解决清冰雪路面高低不平的难题.哈尔滨市交通工程学会四届三次会员代表大会会议文献.哈尔滨:哈尔滨市交通工程学会,2001
[3] 张长荣.道路除雪机的主要结构及其功能.建筑机械化,1999(3):14～15
[4] 李盛林,张宏文.公路快速清雪装置的设计.筑路机械与施工机械化,2002(1):3～4
[5] 张长荣.除雪机配用车辆的选择与应用.建筑机械化,2000(1):41～42
[6] 李盛林,孟祥拴等.国内外除雪装置.筑路机械与施工机械化,2003(1):17～19
[7] 陆季挺,靳长征.高速公路快速除雪作业.筑路机械与施工机械化,1999(2):43～45
[8] 赵恩棠.道路除雪融冰的措施.东北公路,1994(4):28～32
[9] 高存原,陆季挺.道路冰雪清除方法及其应用.山西交通科技,2000(2):52～54
[10] 韩爱民,国委文.CBX—1800 型除冰雪机.农业机械化与电气化,2002(1):37
[11] 杨光,李广鹏.CL3.6 型犁式除雪装置的开发研制.筑路机械与施工机械化,1998(3):10～11
[12] 吾布利.小型除雪机的研制.新疆农机化,2000(6):24
[13] 刘大志.北方高等级公路的除雪方法.筑路机械与施工机械化,1998(6):35～36
[14] 王承惠.永江清雪除冰专用车辆的技术性能和特点.专用汽车,2003(11):54
[15] 唐文初.旋转机械式路面冰雪装置.机械设计,1996(9):28～30
[16] 张长荣.振动压裂式破冰雪机的研制.筑路机械与施工机械化,1999(6):16～17
[17] 李乔非,吴书琴.除雪机除雪部件的实验研究.机械工程师,1995(2):12～13
[18] 王新梅,殷林.可调压式自动越障式除雪装置.工程机械,1999(10):10～11
[19] 马君,陈伟旭等.如何使用 QX—1.4 型清雪机.黑龙江水产,2002(5):23～24
[20] 朱彩霞,夏智武.QX700 型庭院清雪机的研制.新疆农机化,2002(5):31～32
[21] 刘延超,张春宇等.CBX—1500 型冰雪清除机的研制.黑龙江交通科技,2002(1):45～46
[22] 杨光,刘大志.CBX—1600 型除冰雪机的功率计算.筑路机械与施工机械化,1996(2):5～6
[23] 关明慧,徐宇工等.微波加热技术在清除道路积冰中的应用.北方交通大学学报,2003(4):79～83
[24] 马文星,邓洪超.筑路与养护路机械.北京:化学工业出版社,2005
[25] Steinfeld Aaron, Tan Han-Shue,Bougler Benedicte.Naturalistic findings for assisted snowplow operation.Proceedings of the Human Factors and Ergonomics Society,2001:1651～1655
[26] Da Cunha Manuel.County improves its snow and ice control program .Public Works,1996(4):28～29
[27] Olson Walter W., Kempainen, Alan, etc.Dynamic modeling of forces on snowplow equipped trucks.Heavy Vehicle and Highway Dynamics(SAE Special Publications, v 1308, Nov), 1997:49～55
[28] Arnold James A..New applications make NDGPS more pervasive.Public Roads, 2001(4):39～43
[29] Smithson Leland D..Smith, Duane E..Engineering cold regions maintenance equipment for the 21st century.Proceedings of the International Conference on Cold Regions Engineering, 1998: 672～683
[30] McGehee D.V. , Raby, M., Nourse, G.E..Defining the operator interface of a snowplow lane tracking system from field interviews and surveys.Transportation Research Record, 2000(n 1700):38～44
[31] Roosevelt D.S., Hanson, R.A., Campenni, W.M. .Automatic vehicle location system in urban winter maintenance operations.Transportation Research Record, 2001(n 1741): 6～10
[32] 崔宪江编译.除雪机械.北京:人民交通出版社,1988
[33] 余琴.C 波段多极化 SAR 反演积雪湿度模型研究[D].中国科学院遥感应用研究所, 2004
[34] 赵洪勇,吕宾林,朱忠林.声波测试法在铁路路基测试中的应用.铁道建筑,2005(12):45～46
[35] 佐藤和敏,彭惠民译.用超声波探测道口障碍物的道口安全新技术.中国铁路,2003(2):65～68
[36] 雷旭友.岩溶在地质雷达图像和ρs 断面图上的反映特征.中国地质灾害与防治学报,1997(8):143～154
[37] 张河水,陆曙明等.地质雷达在高速公路建设中的应用.公路,1998(8):40～42
[38] 赵楠,王惠芳.地质雷达在工程勘察中的应用.勘察科学技术,1998(3):58～63
[39] 岳建华,何兵涛.超吸收边界条件在地质雷达剖面正演中的应用,中国矿业大学学报,1999(5):453～456
[40] 李张明.地质雷达在三峡工程施工阶段应用研究.CT 理论与应用研究,2000(3):41～46
[41] 李大洪.影响地质雷达工作频率选择的若干因素.矿业安全与环保,2000(6):29～30
[42] 宋雷,黄家会等.电磁波速度层析成像技术用于探测地下深部岩层特性研究.工程勘察,2000(2):62～63
[43] 杨立新.雷达在检测路面厚度中的应用.湖南交通科技,2001(1):18～19
[44] 姬继法,潘纪顺.应用地质雷达探测小型不良地质体.地质与勘探,2002(1):80～82
[45] 陈爱云,方云.地质雷达在石质文物保护工程中的应用.西部探矿工程,2003(8):165～166
[46] 李成香,强建科,王建军.地质雷达在公路裂缝检测中的应用.工程地球物理学报,2004(4):282～286
[47] Annan A P.Design and development of a digital discrete measurement ground penetrating radar system.Paper presented at the working on ground penetrating radar sponsored by the Geological Survey of Canada, Ottawa, 1988
[48] James S Mellett.Ground penetrating radar applications in engineering ,environmental management, and geology[J] .Journal of Applied Geophysics, 1995(33):157～166
[49] Darid F Dominic.Delineation of shallow stratigraphy using ground penetrating radar[J] .Journal of Applied Geophysics, 1995(33):166～170
[50] Kovas A, Morey R M.Electromagnetic measurements of multi-year sea ice using impule radar.Gold Regions Science and Technology, 1986(12):67～93
[51] Kwok P K, Rignot E, Holt B, etc.Identification of sea ice types in spaceborne synthetic aperture radar data.Journal of Geophysical Research,1992(97C2):2391～2402
[52] 金亚秋,陈轶等.雷达卫星 SAR 与防卫气象卫星 SSM/I 对渤海海冰的观测研究.地球物理学报,2001(2):163～170
[53] 孙波,温家洪等.北冰洋冰厚度穿透雷达探测与下表层形态特征分析.中国科学(D 辑),2002(11)951～957
[54] 张宏祥,盛阳.公路地质雷达在检测公路路面厚度中的应用.森林工程,2001(3):37～38
[55] 杨天春,吕绍林,伍永贵.地质雷达检测道路结构的原理及应用分析.中南工业大学学报,2001(2):118～121
[56] 黎滨洪,金荣洪,张佩玉.电磁场与波.上海:上海交通大学出版社,1996
[57] 王秉中.计算电磁学.北京:科学出版社,2002
[58] 谢处方,饶克谨.电磁场与电磁学.北京:北京大学出版社,1987
[59] 曾绍发,刘四新等.探地雷达方法原理及应用.北京:科学出版社,2006
[60] 李大心.探地雷达原理与应用.北京:地质出版社,1994
[61] LTD—2000 探地雷达使用手册.青岛:中国电波传播研究所,2005.03
[62] 王朝英,冯新喜.信号处理原理.北京:清华大学出版社,北京交通大学出版社,2005
[63] 杨建国.小波分析及其工程应用.北京:机械工业出版社,2005
[64] 邹海波,宁书年等.小波理论在探地雷达信号处理中的作用.地球物理学进展,2004(2):268～274
[65] Alexander K Kerimov,Vladimir V Kopeikin.The inverse problem forGPR of impulse type via optional control theory.7th Intern. GPR conf. ,1998(1):309～312
[66] Lazaro-Macncilla O,Gomez-Trevinon E.One-Dimensinal GPR inverse problem.7th Intern. GPR conf. ,1998(1):258～289
[67] Zhixiong Wu,Ce Liu.An image reconstruction method using GPR data.IEEE Transon GPR,1999 (1) :327～334
[68] Admana Bitri,Gilles Grandjecon.Frequency-wave number modeling and migration of 2-D GPR data in moderate heterogeneous dispersive media.Geophysical Prospecting,1998 (3) :387～301
[69] 陈文超,师振盛等.小波变换在去除探地雷达信号直达波的作用.电波科学学报,2000(1):352～357
[70] 肖艳军,李建勋.基于小波变换的信号滤波在探地雷达中的作用.电波科学学报,2006(1):140～145
[71] 邹海林,隋亚莉等.基于小波变换的 GPR 图象去噪方法研究.系统仿真学报,2005(4):855～862
[72] 邓华,蔡迎春等.小波变换在路用雷达信号处理中的应用.郑州大学学报,2002(1):101～103
[73] 詹毅,李修忠等.用连续小波变换分析探地雷达回波信号.物探与化探,1999(6):443～447
[74] 詹毅,梁昌洪等.探地雷达回波信号处理中小波变换域方法研究.西安电子科技大学学报,1999(3):305～309
[75] 陈文超.小波变换应用于探地信号处理的研究[D].西安交通大学,西安:2000
[76] 刘明才.小波分析及其应用.北京:清华大学出版社,2005
[77] 梁学章,何甲兴等.小波分析.北京:国防工业出版社,2005
[78] 孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[79] 彭玉华.小波变换与工程应用.北京:科学出版社,1999
[80] 高静怀,王文秉等.地震资料处理中小波函数的选取研究.地球物理学报,1996(3):392～400
[81] D.J Daniels.Surface pentrating radar.Published by IEE,London,UK,1996
[82] Schneider W.A . Intergral formulation for migration in two and three dimansion.Geophysics,1978,Vol.(43):49～76
[83] Berkhout A.J.Steep dip finite-difference migration.Geophysical prospecting,1979,Vol.(27):196～213
[84] 马在田.地震成像技术.北京:石油出版社,1989
[85] Yu H.,Ying X.,Shi Y..The use of FK techniques in GPR processing.6’th International conference on ground penetrating rader(Japan),1996:595～600
[86] Zhang Anxue,Jiang Yansheng,etc..Experimental sudies on GPR velocity estimation and imaging method using migration in frequency-wavenumber domain.Proceedings ISAPE 2000 5’th international symposium,2000:468～473
[87] 王光力.探地雷达成像方法研究[D].天津:中国民用航空学院,2004
[88] 王光力,吴仁彪.探地雷达 phase-shift 偏移成像算法研究.第九届雷达年会论文集,2006:415～418
[89] 张春城,周正欧.基于 Stolt 偏移的探地雷达合成孔径成像研究.电波科学学报,2004(3):316～320
[90] 陈延梅,邓延权,曲文波.Stolt 45°上行波方程偏移的一种新算法.黑龙江大学自然科学学报,2002(1):21～26
[91] 刘喜武,刘洪.波动方程地震偏移成像方法的现状与进展.地球物理学进展,2002(4):583～589
[92] 孔令讲,周正欧.波速误差对浅地层探地雷达 SAR 算法影响的研究.电波科学学报,2005(6):819～823
[93] 毛宁波,戴塔根.地震成像与空间成像、医学成像的交叉与融合.地球物理学进展,2003(4):643～646
[94] 罗银河,刘江平,俞国柱.叠前深度偏移述评.物探与化探,2004(6):540～545
[95] 金胜汶,许士勇,吴如山.基于波动方程的广义屏叠前深度偏移.地球物理学报,2002(5):1～7
[96] 丁伟,李振春,刘玉莲.基于近似的联合叠前深度偏移方法.石油物探,2003(1):29～34
[97] 尹军杰,刘学伟等.基于散射成像数值模拟的地震采集参数论证.石油物探,2005(1):58～64
[98] 姚萌,刘树人,杨燕.基于探地雷达的古墓遗址探测及数据后处理方法.遥感学报,2001(4):317～320
[99] 刘振春,刘玉莲等.基于最优 Born 近似的叠前深度偏移方法.石油大学学报(自然科学版),2002(5):32～36
[100] 范立生,高明星等.极化 SAR 遥感中森林特征的提取.电波科学学报,2005(5):553～556
[101] 张钋,李幼铭,刘洪.几类叠前深度偏移方法的研究现状.地球物理学进展,2000(2):30～39
[102] 黄伟,邓世坤.偏移方法用于探地雷达图像处理的有效性研究.地质科技情报,2001(4):99～102
[103] 王棣,王华忠等.频率波数域共偏移距叠前时间偏移方法.石油物探,2004(1):8～10
[104] 周辉,王兆磊等.同时实现地质雷达数据地形校正和偏移成像的方法.吉林大学学报(地球科学版),2004(3):459～463
[105] 何樵登.地震勘探原理与方法.北京:地质出版社,1986
[106] 吴明赞,王志功.基于 ADSP 的小波图像增强算法实现.计算机应用研究,2006(6):160～164
[107] 郑慕之,陶玲,王惠南.MRI 图像的多尺度小波增强新算法.中国医疗器械信息,2004(4):9～11
[108] 曾鹏鑫,么健石等.基于小波变换的图像增强算法.东北大学学报(自然科学版),2005(6):527～530
[109] 张瑾,陈向东.小波分析在遥感图像增强中的应用及 MATLAB 实现.半导体光电,2005(3,增刊):149～151
[110] 赵选科,赵庆武,沈晓芳.基于联合变换相关的光电小波实时图像边缘增强.光电子技术与信息,2004(3):55～57
[111] 李郁,闫敬文.一种基于小波分析的 SAR 图像增强去噪方法.计算机工程与设计,2004(2):309～310
[112] Ji Shu-peng,Ding Xiao-qing.Morphological filters and wavelet-based histogram equalization image enhancement for weak target detection.Infrared technology,2003(4):32～38
[113] 成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004
[114] A. Giannopoulos.Modelling ground penetrating radar by GprMax.Construction and Building Materials,2005(19): 755～762
[115] Antonis Giannopoulos.GprMax2D/3D User's Manual Version 2.0.USA:2005
[116] 粟毅,黄春琳,雷文太.探地雷达理论与应用.北京:科学出版社,2006
[117] 吴超.基于路面探测信息的清雪车自动控制研究[D].长春:吉林大学,2006
[118] 高文森,张魁元等.概率统计教程.长春:东北师范大学出版社,1999
[119] 王栋梁等.非对称阀控制非对称缸的分析研究.山东建材学院学报,2001(6) :123～127
[120] 高钦和,黄先祥.基于 simulink 的重物举升液压控制系统建模与仿真.机床与液压, 2001(1):61～63
[121] 付永领,祁晓野. AMESim 系统建模和仿真—从入门到精通.北京:北京航空航天出版社,2006
[122] 飞思科技产品研发中心.神经网络理论与 MATLAB7 实现.北京:电子工业出版社,2005