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Proceedings Paper

Fuzzy set information fusion in land mine detection
Author(s): Bruce N. Nelson; Paul D. Gader; James M. Keller
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Paper Abstract

A robust method of performing information fusion in processing ground penetrating radar (GPR) sensor data in landmine detection will be described. The method involves running multiple automatic target recognition algorithms (ATRs) in parallel on the GPR data. The outputs from each of the ATRs are spatially correlated and a feature set for each potential radar target is automatically generated. The feature set is provided as input to Mamdani style fuzzy inference systems. The fuzzy inference systems' output is a mine confidence value. The major advantage of this technique is that it provides consistent mine detection performance independent of road type, GPR hardware settings, and ATR setup parameters. This paper will first describe the individual ATRs and the process of spatially correlating target reports and generating a feature set. This will be followed by a description of the fuzzy inference system used for target classification. THe paper will conclude with test result from various Fort AP Hill calibration mine lanes.

Paper Details

Date Published: 2 August 1999
PDF: 11 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.356997
Show Author Affiliations
Bruce N. Nelson, Geo-Centers, Inc. (United States)
Paul D. Gader, Univ. of Missouri/Columbia (United States)
James M. Keller, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 3710:
Detection and Remediation Technologies for Mines and Minelike Targets IV
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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