Feature extraction and processing of spatial frequency-domain electromagnetic induction sensor data for improved landmine discrimination

Frequency-domain electromagnetic induction (EMI) sensors have been shown to provide target signatures which enable discrimination of landmines from harmless clutter. In particular, frequency-domain EMI sensors are well-suited for target characterization by inverting a physics-based signal model. In many model-based signal processing paradigms, 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. When spatial data is available, the diversity of the measured signals may provide more information for estimating the basis function parameters. After model inversion, the basis function parameters can be used as features for classifying the target as landmine or clutter. In this work, feature extraction from spatial frequency-domain EMI sensor data is investigated. Results for data measured with a prototype frequency-domain EMI sensor at a standardized test site are presented. Preliminary results indicate that Structured relevance vector machine (sRVM) regression model inversion using spatial data provides stable, and sparse, sets of target features. © 2012 SPIE.

Duke Scholars

Author Stacy L. Tantum Electrical and Computer Engineering

Author Leslie M. Collins Electrical and Computer Engineering