Research On Signal Processing Of GPR Detecting Snow-Cover Road Surfaces And Automatic Control Of The Snow Shovel

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 MethodThe 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 SignalsThe 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 SignalsFrom 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 MatchFrom 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 ThicknessAlthough 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.