Measures for evaluating sea-mine identification processing performance and the enhancements provided by fusing multi-sensor/multi-process data via an M-out-of-N voting scheme

摘要

This paper indicates how sea=test data collected by N independent sensors - or alternatively, data collected by a single sensor, but processed through N independent processing strings - can be used to model, estimate, and predict the performance of a mine identification system. The proposed procedure exploits the information supplied by the sensors/processes (namely, the locations of their individual detection reports), to approximate the probabilities of detection and false alarm in terms of the ratios of the numbers of reports, as seen by the various combinations of sensors. A constrained Least-Squares procedure, fitting the products of these ratios as dictated by their independence equivalencies, is then used to estimate the individual sensor/process probabilities of detection, of false alarm caused by mine-like objects, and of false alarm due to noise. We can then obtain the corresponding probabilities that can be expected after fusing the data with an M-out-of-N voting process.