| Project/Area Number |
15560300
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Electron device/Electronic equipment
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| Research Institution | Kumamoto University |
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Principal Investigator |
NISHIMOTO Masahiko Kumamoto University, Electrical and Computer Engineering, Professor, 工学部, 教授 (60198520)
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| Co-Investigator(Kenkyū-buntansha) |
IKUNO Hiroyoshi Kumamoto University, Electrical and Computer Engineering, Professor, 工学部, 教授 (80040400)
NAKA Yoshihiro Kumamoto University, Graduate School of Science and Technology, Research Associate, 大学院・自然科学研究科, 助手 (30305007)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2004: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2003: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | landmine detection / ground penetrating radar / pattern recognition / feature extraction |
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| Research Abstract |
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 extraction
In 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 algorithm
For 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 generation
In 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 performance
We 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.
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