Combining magnitude and phase features in acoustic landmine detection

The utility of acoustic-to-seismic coupling systems for landmine detection has been clearly established. They have been shown to be able to detect very low metal content landmines that are difficult to detect for ground-penetrating radars. This technique measures the difference in ground vibration velocity of regions with and without buried landmines when subject to acoustic excitation. For most applications, only the magnitude of the surface velocity is used to construct recognition algorithms. We recently introduced phase-based features in the classification scheme, significantly reducing false alarms at given detection probabilities. Here the focus is on the analysis of ground velocity data collected in the time domain with a moving array of laser Doppler vibrometers. The processing techniques used to extract the magnitude and phase information are described, as well as examples to demonstrate how combined magnitude and phase features help improve the detection of buried mines with weak signatures and reduce false alarms. Simple mass-and-spring models are shown to be useful in understanding the observed features. Also analyzed are the phase signatures of a number of ground regions with buried man-made clutter objects and how they can be useful in separating these clutters from actual landmine regions.