Classification of closely spaced subsurface objects using electromagnetic induction data and blind source separation algorithms

Most research in the subsurface object identification area assumes that objects are well isolated from each other and thus that a single signature is measured by the sensing system. In the scenario where multiple closely spaced subsurface objects are present within the field of view of the sensor, the signals measured using electromagnetic induction sensors are mixed, and the mixed measurements cannot be used to determine the identity of each of the individual objects using conventional techniques. Since only the mixed observations are available, and these are usually available at multiple target/sensor orientations, separating individual signals from the set of mixtures can be posed as a blind source separation (BSS) problem. In this paper we consider two approaches to source separation, one based on the second-order statistics and the other based on the fourth-order statistics. Following the source separation, object classification performance is obtained using the separated sources and a Bayesian classifier. We analyze the strengths and weaknesses of each BSS approach and compare their performance.