Automating the detection process in acoustic-seismic landmine detection speeds up the detection process
and eliminates the need for a human operator in the minefield. Previous automatic detection algorithms for
acoustic landmine detection showed excellent results for detecting landmines in various environments. However, these algorithms use environment-specific noise-removal procedures that rely on training sets acquired over mine-free areas. In this work, we derive a new detection algorithm that adapts to varying conditions and employs environment-independent techniques. The algorithm is based on the generalized likelihood ratio (GLR) test and asymptotically achieves a constant false alarm rate (CFAR). The algorithm processes the magnitude and phase of the vibrational velocity and shows satisfying results of detecting landmines in gravel and dirt lanes.
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