Sparse model representations of target signatures for improved landmine detection using frequency-domain electromagnetic induction sensors

Abstract

Frequency-domain electromagnetic induction (EMI) sensors have the ability to measure target signatures which enable discrimination of landmines from harmless clutter. In a model-based signal processing paradigm, the target signatures can be decomposed into a weighted sum of parameterized basis functions, where the basis functions are intrinsic to the target under consideration and the associated weights are a function of the target sensor orientation. The basis function parameters can then be used as features for classification of the target as landmine or clutter. One of the challenges associated with effectively utilizing frequency-domain EMI sensor data within a model-based signal processing paradigm such as this is determining the correct model order for the measured data, as the number of basis functions intrinsic to the target under consideration is not known a priori. In this work, relevance vector machine (RVM) regression is applied to simultaneously determine both the number of parameterized basis functions and their relative contributions to the measured signal. The target may then be classified utilizing the basis function parameters as features within a statistical classifier. Results for data measured with a prototype frequency-domain EMI sensor at a standardized test site are presented. Preliminary results indicate that RVM regression followed by statistical classification utilizing the resulting model-based features provides an effective approach for classifying targets as landmine or clutter.