1. School of Electronics and Information Engineering,Heilongjiang University of Science and Technology,Harbin　 150022,China; 2. School of Electrical and Control Engineering,Heilongjiang University of Science and Technology,Harbin　 150022,China; 3. Laboratory of Information Network Center,Heilongjiang U-niversity of Science and Technology,Harbin　 150022,China

超宽带在井下穿透成像算法中需要解决的关键问题,是在塌方体电磁参数未知的情况下,对埋藏在塌方体另一侧的目标进行成像。由于巷道内背景介质复杂的电磁特性以及多径传播效应等物理现象的影响,很难对矿井下超宽带探测成像系统性能进行有效预测和分析,因此提出了一种射线追踪算法(Ray-tracing)和SVM相结合的方法。该方法通过Ray-tracing算法得到穿透塌方体成像的样本数据,再利用SVM进行分类,解决了矿井下塌方体后埋藏目标检测的问题。仿真结果表明,该方法消除了反演过程中的非线性和病态性,可以实现塌方体后的未知目标成像。

The key problem that the UWB in the underground imaging algorithm needs to be solved is to image the tar- get buried on the other side of the cave body with the unknown electromagnetic parameters. It is difficult to effectively predict and analyze the performance of UWB detection imaging system in mine because of the influence of physical phenomena such as complex electromagnetic properties and multipath propagation effects in the backlash of roadway. Therefore,a method to combine a ray tracing algorithm (Ray-tracing) and SVM was proposed. This method uses Ray- tracing algorithm to obtain the sample data of penetrating the image of the roof collapse,and then classifies it with SVM to solve the problem of buried target recognition after the roof collapse of the mine. The simulation results show that the method can eliminate the non-linearity and ill-posedness in the inversion process,and can realize the unknown target imaging after the roof collapse.

GUO Jikun,ZHAO Qing,XU Feng. Research on ultra wide-band penetration imaging algorithm for coal mine based on SVM[J]. Journal of China Coal Society,2018,43(2):584-590.