Development of high performance landmine classifier for ground penetrating radar

本研究旨在开发一种高性能的探地雷达地雷分类器。使用探地雷达传感器系统的地雷探测过程分为两个阶段。第一阶段是发现阶段，在此阶段，所有类型的埋藏物都被定位。第二阶段是识别阶段，将地雷与其他物体如石头、金属碎片等区分开来。在本研究中，我们提出了一种高性能的分类算法，并开发了一个简单的用于识别阶段的分类器原型。主要研究成果如下：首先，提出了一种基于匹配追踪的数据预处理方法，精确地去除了地杂波。在去除杂波的影响后，从时域和频域提取了几种有效的地雷分类特征。接下来，我们采用似然比测试和支持向量机（SVM），这是一种新型的分类算法作为分类方法。为了评估地雷歧视的性能，我们进行了蒙特卡洛模拟使用数值模拟产生的数据集。从模拟，我们证实，足够的歧视性能，即使埋在浅的深度地雷，也检查了土壤条件（即表面粗糙度，不均匀性，土壤中的水分）和地雷的倾斜度对歧视性能的影响。此外，我们提取的特征，从测量数据中获得的GPR系统在本研究中开发，并确认所提出的分类实际问题的适用性。

The purpose of this research is to develop a high performance landmine classifier for ground penetrating radar (GPR) sensor. A process of landmine detection using GPR sensor systems is divided into two stages. The first stage is the find stage, where all the various types of buried objects are located. The second stage, the identification stage, differentiates landmines from other objects such as stones, metal fragments, and so on. In this research, we have proposed a high performance classification algorithm and have developed a simple prototype of classifier used in identification stage. Results of the research are summarized as follows:First, we introduced a method for data preprocessing based on a Matching Pursuits and accurately removed ground clutter. After the removal of the clutter contribution, we extracted several kinds of effective features used for landmine classification from both time and frequency domains. Next, we employed a likelihood ratio test and a support vector machine (SVM) that is a novel type of classification algorithm as classification methods. In order to evaluate the performance of landmine discrimination we carried out Monte Carlo simulations using dataset generated by numerical simulations. From the simulation, we confirmed that sufficient discrimination performance was obtained even for landmines buried at shallow depths, and also checked the effects of soil condition (i.e. surface roughness, inhomogeneity, and moisture in soil) and inclination of the landmine on discrimination performance. Furthermore, we extracted the features from measured data obtained by the GPR system developed in this research and confirmed applicability of the proposed classifier to actual problems.