| Budget Amount *help |
¥3,730,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2006: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Research Abstract |
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.
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