Opimization of the magnetic anomaly signals from a new land mine detection device

Abstract

Magnetic optimization studies have been carried out for a new magnetic anomaly (MA) device in order to detect the land mines. This device will use an artificial neural network algorithm for the classification of magnetic materials to diminish the mines from other magnetically detectable bodies. Therefore an efficient planar sensor network has been constructed in order to detect the field anomalies. The device operates fast and requires some preliminary tests to increase the measurement accuracy and reliability. In this manner, the magnetic responses of different materials should be ascertained for different distances to MA device. It operates under 24 ACV and includes a square looped Helmholtz coil system and plenary arranged 20 small field detection coils. Preliminary optimization studies have proven that the MA device can diminish the magnetic materials and its reliability can be enhanced further by measuring different material types in order to produce a solid operation achieve for diminishing the mines according to the neural network learning.