Development of high performance classification algorithm for ground penetrating radar systems used in landmine detection

在这项研究中，我们已经开发了一个高性能的分类算法，用于探地雷达（GPR）系统在地雷探测。地雷探测过程分为两个步骤。第一步是寻找阶段，所有类型的埋藏物都被定位。第二阶段，即识别阶段，然后利用参考数据将地雷与石头和其他物体区分开来。在这项研究中，我们考虑了识别阶段。本文的主要研究成果如下：1.地杂波去除与特征提取为了从探地雷达数据中去除杂波，本文提出了一种基于匹配追踪的方法，该方法的字典元素是变形的入射脉冲。在去除杂波后，从残留信号中提取了与波相关、能量比和信号到达时间相关的三种目标特征。2.高性能分类算法对于目标分类，提出了两种基于隐马尔可夫模型理论和似然比检验的分类算法3.数据生成数值方法的改进为了提高性能，需要使用FDTD方法生成的数据集进行Monte Carlo模拟。由于它需要大量的计算时间和存储容量，产生数百个模拟数据，我们已经改进了数值方法。4.性能评估我们已经提出了接收机工作特性曲线的形式的分类性能，并已表明，良好的分类性能已经获得，即使是埋在浅埋在粗糙地面下的地雷。还应利用通过实地试验获得的实际探地雷达数据进行性能评价。这一重要的研究问题目前正在调查中。

In this research, we have developed a high performance classification algorithm for ground penetrating radar(GPR) systems used in landmine detection. A process of landmine detection is divided into two steps. The first step is the find stage, where all the various types of buried objects are located. The second stage, the identification stage, then differentiates landmines from stones and other objects using reference data. In this research, we have considered the identification stage. Results of this research are summarized as follows :1.Ground clutter removal and feature extractionIn order to remove clutters from the GPR data, we have proposed a method based on a Matching Pursuits with a dictionary whose elements are deformed incident pulses. After the removal of the clutters, we have extracted three kinds of target features related to wave correlation, energy ratio, and signal arrival time from the residual signals.2.High performance classification algorithmFor target classification, we have proposed two classification algorithms based on a theory of hidden Markov models and a likelihood ration test.3.Improvement of numerical method for data generationIn order to evaluate its classification performance, a Monte Carlo simulation using dataset generated by an FDTD method is required. Since it takes large computation time and storage capacity for generating hundreds of simulation data, we have improved the numerical method.4.Evaluation of performanceWe have presented the classification performance in the form of receiver operating characteristics curves and have shown that good classification performance has been obtained, even for landmines buried at shallow depths under rough ground surfaces. Performance evaluation using actual GPR data obtained through field experiments should also be undertaken. This important research problem is currently under investigation.