Publications

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The rate at which a mine detection system falsely identifies man-made or natural clutter objects as mines is referred to as the system's false alarm rate (FAR). Generally expressed as a rate per unit area or time, the FAR is one of the primary metrics used to gauge system performance. In this report, an overview is given of statistical methods appropriate for the analysis of data relating to FAR. Techniques are presented for determining a suitable size for the clutter collection area, for summarizing the performance of a single sensor, and for comparing different sensors. For readers requiring more thorough coverage of the topics discussed, references to the statistical literature are provided. A companion report addresses statistical issues related to the estimation of mine detection probabilities.

The problem of combining multi-source information in applications related to automatic target recognition (ATR) is addressed. A mathematical approach is proposed for fusing the (possibly dependent) outputs of multiple ATR systems or algorithms. The method is derived from statistical principles, and the fused decision takes the form of an hypothesis test. The distribution of the test statistic is approximated as gamma, with parameters estimated from available training data. In a brief simulation study, the proposed method outperforms several alternative fusion techniques.