Using mode shapes and natural frequencies in de-noising and classification of acoustic-seismic data for landmine detection

Abstract

Most of the research on developing automatic target recognition (ATR) algorithms for acoustic-seismic landmine detection platforms has been focused on using geometric features, such as size and shape, of anomaly to distinguish between mines and clutter. This approach has achieved some success especially in detecting larger anti-tank mines. However, for smaller anti-personnel landmines, the difference in geometric features between mines and clutter can be very small, if any. To improve the detection vs. false alarm rates, it is necessary to incorporate other features into the ATR process. It has been observed from the collected acoustic data that areas with buried mines reveal more complicated surface vibration structures, such as the ring-like pattern, at certain frequencies than what a one-dimensional lumped mass-spring-dashpot model can describe. In this paper, we utilize the distributed mine/soil interaction model developed by the University of Mississippi to describe the surface vibration patterns. We develop a modified Hankel transform to extract features from areas under interrogation. Under such transform, concentration of energy is closely related to an object's physical properties. The frequency at which the energy concentration occurs corresponds to the object's natural frequency, while the corresponding Bessel basis captures its mode shape. After de-noising the transformed data, we use the frequencies, Bessel bases, and magnitudes of the energy concentrations, together with other geometric features, to form the feature vectors. We tested these features on a dataset consisting of anti-tank and anti-personnel mines as well as blank areas and metallic and non-metallic clutter. Classifiers designed based on the combined geometric and model-based features perform significantly better than those based on the geometric features alone.