Matched neural filters for EMI based mine detection

Remedial mine detection and the detection of unexploded ordnance (UXO) have become very important for humanitarian reasons. This paper addresses mine detection using commonly used electromagnetic induction sensors. We propose and evaluate two neural network approaches to mine detection which provide a robust nonparametric technique, based on training the networks using data from a previously calibrated portion of the minefield, or from a similar minefield. In the first approach, we combine a novel statistic, the S-statistic (which is a real valued variable related to the relative energy difference measured around a point in the minefield) with the /spl delta/-technique in a random neural network (RNN) design. In the second approach, a RNN is trained using a 3/spl times/3 block measurement window, and then applied as a postprocessor for the /spl delta/-technique. This RNN has an unconventional feedforward structure which realizes a matched filter to discriminate between nonmine patterns and mines. Experimental results for both approaches show that the RNN reduces false alarms substantially over the /spl delta/-technique and the energy detector.