Anti-Personnel Land Mine Detection and Discrimination Using Acoustic Data

Abstract

An acoustic-based land mine detection system was field-tested by the University of Mississippi with promising results. This system uses a Laser Doppler Vibrometer (LDV) to measure the velocity of the vibration at the surface of the soil induced by acoustic energy in various frequency bands. In this paper, automated methods for detecting and discriminating Anti-Personnel (AP) mines from clutter objects are presented. Pre-processing methods rely on nonlinear filters realized as Choquet integrals. These filters are robust to the non-Gaussian, impulse type noise in this type of data. Detection follows pre-processing and relies on adaptive thresholding over each frequency band and three-dimensional (3D) connected component analysis. Features are extracted from the 2D frequency slices of the 3D connected components. The features are adaptively aggregated over frequency and used for discriminant analysis. Experiments are performed using anti-personnel mines, clutter objects, and blank areas (background samples with no mines or clutter objects). The algorithm detects 92% of the mines for a wide range of parameters. For some threshold values, 100% of the mines are detected and 92% of the mines are classified as mines with no false alarms.