Classification of landmine-like metal targets using wideband electromagnetic induction

Abstract

Abstract-In our previous work, we have shown that the detectability of landmines can be improved dramatically by the careful application of signal detection theory to time-domain electromagnetic induction (EMI) data using a purely statistical approach. In this paper, classification of various metallic landmine-like targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both a theoretical model of the response of such a sensor and the uncertainties regarding the target/sensor orientation is developed. This allows the algorithms to be trained without an extensive data collection. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over a standard approach, which utilizes the signature obtained when the sensor is centered over the target and located at the mean expected target/sensor distance, and thus ignores the uncertainties inherent in the problem. On the average, a 60% improvement is obtained.

A method based on relaxation frequency for object recognition

“…We can extract the relaxation frequency discrete spectrum and features of low metal content, medium metal content, metal mines, and metal debris clutter according to the data vectors obtained. Relaxation frequency is independent of the relative spatial orientation and burial depth between targets and detectors [5] . This physical characteristic helps identify substance type and improve detection efficiency, and it is the main development direction of intelligent metal mine detection technology in the future.…”