Automated identification of buried landmines using normalized electromagnetic induction spectroscopy

Abstract

Electromagnetic induction spectroscopy (EMIS) is used to identify a buried metallic object such as a landmine, based on its EMI spectrum. EMIS, however, depends on the object's electrical conductivity, magnetic permeability, shape, size, depth, and orientation. For a given mine made of specific metals, shape, and size, however, the only variables are the mine's burial depth and orientation. This paper describes a method of identifying a landmine using a normalized EMIS spectrum, which is independent of the orientation or depth. We assume that the target is small compared with its distance to or size of the sensor so that the source field at a target is uniform. In this case, the normalized spectrum will be range-independent and, therefore, the target identification is based on only spectral shapes. We have developed and tested an algorithm that matches a normalized EMIS spectrum to those of library targets. We applied the new process to 1) numerically simulated data, 2) experimental data from controlled sites using inert mines, mine simulants, and clutter items, and 3) finally extensive field data collected at a blind test site established by the U.S. Army. Test results show that targets are correctly identified with a misfit of less than 10%; they also show that 80% of clutter may be rejected, based on a misfit over 30%.