This paper examines a technique for detection of antipersonnel mines with varied unknown depths. The method attempted in this study is based on a subtractive fuzzy logic algorithm. A comparison of the false alarm rate, the detection rate as well as the error rate is used to test the performance of the detection scheme in cases where the mine depth is both known and unknown. The effect of the a priori knowledge of the data on the execution of the detection scheme is observed, as well as the effect of the SNR level used to train the fuzzy logic detector. The algorithm is tested using real GPR data representing anti-personnel nonmetallic mine and other objects such as stone, brick, or a metallic sphere.
Detection of nonmetallic antipersonnel mines with unknown depth is the focus of this study. We compare the performance of two possible detection schemes. The first is based on wavelet decomposition, and the second relies on signal extraction using blind source separation techniques. The detection performance is measured in terms of the probability of false alarm and the probability of detection. The impact of mine depth knowledge on detector performance is examined. The data utilized is the Ground Penetrating Radar (GPR) data provided by the Demining Technology Center (DeTeC). Only B-scan data is used in the experimental phase of this study.