For the landmine detection problem, a detector that provides a high probability of detection and a low probability of false alarm is needed. It is often the case that detectors satisfy one requirement at the cost of poor performance with regard to the other. Single sensors cannot achieve this goal, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines, etc. Thus, we consider two types of sensors, EMI and GPR. Time-domain EMI has been extensively used in the military and humanitarian demining. However, it is essentially a metal detector, thus, can detect mines with high metal content successfully, as well as metal debris in the environment. This yields poor detection performance on mines with low metal content and high false alarm rate if the field was contaminated by metallic clutter. On the other hand, GPR is a potential tool for landmine detection, since it can detect and identify subsurface anomalies. A GPR system with wide frequency band can achieve good resolution and adequately deep penetration for landmine detection. In our previous work, we have shown that Bayesian detection approach can be applied to EMI data and provide promising results. In this paper, we present results that indicate that statistical signal processing techniques can improve performance over the conventional detection methods.
Time-domain metal detector and GPR data processing and fusion for landmine detection
2000
6 Seiten, 6 Quellen
Conference paper
English
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