Random neural network filter for land mine detection

The two primary measures of land mine detection performance are the probability of detection P_d and the probability of false alarm P_fa. These two measures are highly inter-dependent and must be evaluated together. The relationship between the two probabilities directly affects the overall performance of the sensor in the field. In this paper we introduce a novel false alarm non-parametric filter based on the random neural network (RNN) model and the δ-technique. The study is based on mine detection using electromagnetic induction (EMI) sensors. The minefield data are pre-processed via the δ-technique before applying it to the RNN. The RNN has a predefined structure that tries to implement a mapping close enough in some precise sense to the discrimination function between non-mine and mine patterns. Limited numbers of non-mine and mine patterns, extracted from a small calibration area for a certain minefield provided by DARPA, are used for training the RNN. We show that the RNN gives effective decisions on patterns measured on other locations using different EMI sensor. The results show that the RNN produces a probability of detection up to 100 per cent with a substantial reduction of false alarms over the δ-technique (up to 40 per cent false alarm filtering)