Bayesian probability analysis for acoustic–seismic landmine detection

Abstract

Landmines buried in the subsurface induce distinct changes in the seismic vibration of the ground surface when an acoustic source insonifies the ground. A scanning laser Doppler vibrometer (SLDV) senses the acoustically-induced seismic vibration of the ground surface in a noncontact, remote manner. The SLDV-based acoustic-to-seismic coupling technology exhibits significant advantages over conventional sensors due to its capability for detecting both metal and nonmetal mines and its stand-off distance. The seismic vibration data scanned from the SLDV are preprocessed to form images. The detection of landmines relies primarily on an analysis of the target amplitude, size, shape, and frequency range. A parametric model has been established [Xiang and Sabatier, J. Acoust. Soc. Am. 110, 2740 (2001)] to describe the amplified surface vibration velocity induced by buried landmines within an appropriate frequency range. This model incorporates vibrational amplitude, size, position of landmines, and the background amplitude into a model-based analysis process in which Bayesian target detection and parameter estimation have been applied. Based on recent field measurement results, the landmine detection procedure within a Bayesian framework will be discussed. [Work supported by the United States Army Communications–Electronics Command, Night Vision and Electronic Sensors Directorate.]