Robust Estimation of Metal Target Shape Using Time-Domain Electromagnetic Induction Data

Abstract

Discriminating metal parts of buried hazardous targets from ordinary metallic clutter is a very difficult and time-consuming task. For that purpose, electromagnetic induction (EMI) sensors composed of multiple transmitter/receiver coils in multiaxis arrangements are commonly used. In this way, data of high fidelity and spatial diversity are obtained so that the target can be characterized in terms of its geometric and electromagnetic properties. However, this often increases sensor size and reduces its portability, which is a problem for humanitarian demining applications, where compact, robust, and lightweight sensors are needed. If such sensors are to be used for target characterization, robust estimation algorithms are required, capable of coping with limited spatial information content and uncertainties related to sensor positioning and its coil geometry model. In this paper, we present a robust concept for estimating the general shape of magnetic metal targets using time-domain EMI sensors with single-axis coil geometries. We introduce the signature matrix, a parameter derived from time-dependent eigenvalues of the target’s magnetic polarizability tensor, and use it for shape estimation. The proposed method was evaluated both through simulations and experiments, using two different sensor platforms (laboratory-based experimental sensor platform and a commercial metal detector mounted on a mobile robot). The obtained results clearly indicate that the target shape can be estimated from sensor data of limited spatial diversity and under the uncertainties of sensor positioning and coil geometry.