<title>Linear density algorithm for patterned minefield detection</title>

Abstract

Given a set of {(x,y)} coordinates, some corresponding to mine locations and the rest corresponding to the locations of minelike clutter, an algorithm is developed which attempts to recognize linear patterns in the data, to filter out clutter, and declare a region as being a minefield or not a minefield. A linear density is computed for each observation at multiple directions. High densities as well as frequently occurring directions are statistics computed for minefield detection as well as pattern recognition for locating minelines. Significance and power curves are developed by Monte Carlo simulation under the assumption that the observed clutter is distributed uniformly over the area scanned. Some limited results on real minefield data are then presented.