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Proceedings Paper

Measures for evaluating sea mine identification processing performance and the enhancements provided by fusing multisensor/multiprocess data via an M-out-of-N voting scheme
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Paper Abstract

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.

Paper Details

Date Published: 11 September 2003
PDF: 12 pages
Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); doi: 10.1117/12.487774
Show Author Affiliations
Manuel F. Fernandez, Lockheed Martin Corp. (United States)
Tom Aridgides, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 5089:
Detection and Remediation Technologies for Mines and Minelike Targets VIII
Russell S. Harmon; John H. Holloway; J. T. Broach, Editor(s)

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