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Proceedings Paper

Families of statistics for detecting minefields
Author(s): Douglas E. Lake
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Paper Abstract

Detecting minefield point patterns is an important problem for the Navy, Marines and Army. Because of the difficulty and uncertainty associated with accurately modeling enemy mine laying procedures, robust and flexible family of statistics are needed to detect minefields as deviations from complete spatial randomness. In this paper, a large family of minefield detection statistics are presented and compared using their asymptotic relative efficiency for testing multinomial and minefield mixture alternatives. A slightly modified version of the widely-used power-divergence statistics are introduced that are appropriate under sparseness assumptions. This family includes the empty boxes test which has been advocated previously as a simple and effective approach. Another family, called VC statistics, is presented that provides a low- complexity statistic with optimal performance. The efficiency of these methods are compared analytically and with a minefield benchmark used in previous work.

Paper Details

Date Published: 4 September 1998
PDF: 12 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324144
Show Author Affiliations
Douglas E. Lake, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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