THE SUPPORT VECTOR MACHINE FOR DIELECTRIC TARGET DETECTION THROUGH A WALL

Abstract

In this paper, a novel approach based on the support vector machine (SVM) for dielectric target detection in through-wall scenario is proposed. Through-wall detection is converted to the establishment and use of a mapping between backscattered data and the dielectric parameter of the target. Then the propagation efiects caused by walls, such as refraction and speed change, are included in the mapping that can be regressed after SVM training process. The training and testing data for the SVM is obtained by flnite-difierence time-domain (FDTD) simulation. Numerical experiments show that once the training phase is completed, this technique only needs computational time in an order of seconds to predict the parameters. Besides, experimental results show that good consistency between the actual parameters and estimated ones is achieved. Through-wall target tracking is also discussed and the results are acceptable.