Detection of random minefields in clutter

The performance of optimum minefield detection tests for unpatterned minefields is determined in this paper. A replacement model for an unpatterned (scatterable) minefield is defined. The log-likelihood ratio for minefield detection is then derived from the model, and detection performance (receiver operating characteristics) determined. The model is validated by a comparison with empirical results for data acquired with the Marine Corps Coastal Battlefield Reconnaissance and Analysis (COBRA) passive multispectral electro-optical sensor. Work with data from the US Army Remote Minefield Detection System (REMIDS) program is also discussed. The replacement model is extended to model the effect of clutter on minefield detection performance; clutter has a substantial impact on performance. Several issues for minefield detection are discussed in detail. First, the notion of jointly sufficient statistics for unpatterned minefield detection is defined. This leads to the categorization of previous work in minefield detection into hard decision and soft decision techniques. The soft decision technique is based on the idea of aggregating individual mine likelihoods over a candidate minefield region, as opposed to hard thresholding the individual mine calls and using the density of detected mines to determine the presence or absence of a minefield. The performance analysis is based on an application of the Central Limit Theorem; these results are validated by simulation, and in a particular case, by an exact computation of the relevant false alarm probabilities.