基于目标物理特征和压缩感知的地雷目标鉴别方法

通过压缩感知(Compressed Sensing, CS)算法可以实现对目标的稀疏成像，并获取其空间散射结构用于目标鉴别和识别。该文针对穿透地表成像的前视超宽带虚拟孔径雷达(Forward Looking Virtual Aperture Radar, FLVAR)实测数据，以CS理论为基础对地雷目标进行稀疏成像，利用地雷目标电磁散射的稀疏性实现其散射结构的提取，将目标散射特性转化为与物理结构相关的几何特征，并基于该特征进行目标的分类鉴别。新方法不仅拓展了地雷鉴别的新思路，而且也为压缩感知在目标散射结构提取和目标鉴别上的应用进行了初步有效的尝试。

A Physics-based Landmine Detection Approach with Compressive Sensing

The Compressed Sensing(CS) technique is an effective approach for sparse imaging and extraction of scattering structure of targets,which can be applied to target discrimination and recognition.Based on the ex-perimental data from the Forward Looking Virtual Aperture Radar(FLVAR) system,the scattering structures of landmines can be acquired by CS sparse imaging algorithm.Then the sparse scattering structures are param-eterized to form the features exploited by classifiers later.In this paper,a novel approach to target discrimination is proposed,which transforms the scattering of landmines to geometrical features,which have strong relationship with its physical characteristics.This new approach not only broadens the methodology for landmine discrimi-nation,but also explores a new way of applying sparse scattering structures to target discrimination.