Title

Matched Neural Filters For Emi Based Mine Detection

Abstract

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 non-parametric 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 recently published δ-Technique in a Random Neural Network (RNN) design. In the second approach, a RNN is trained using a 3×3 block measurement window, and then applied as a post-processor for the δ-Technique. This RNN has an unconventional feedforward structure which realizes a matched filter to discriminate between non-mine patterns and mines. Experimental results for both approaches show that the RNN reduces false alarms substantially over the δ-Technique and the energy detector.

Publication Date

12-1-1999

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

5

Number of Pages

3236-3240

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0033308531 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033308531

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