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Proceedings Paper

Present and future methods of mine detection using scattering parameters and an artificial neural network
Author(s): Gregory Plett; Takeshi Doi; Don Torrieri
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Paper Abstract

The detection and disposal of anti-personnel landmines is one of the most difficult and intractable problems faced in ground conflict. This paper first presents current detection methods which use a separated aperture microwave sensor and an artificial neural-network pattern classifier. Several data-specific pre-processing methods are developed to enhance neural-network learning. In addition, a generalized Karhunen-Loeve transform and the eigenspace separation transform are used to perform data reduction and reduce network complexity. Highly favorable results have been obtained using the above methods in conjunction with a feedforward neural network. Secondly, a very promising idea relating to future research is proposed that uses acoustic modulation of the microwave signal to provide an additional independent feature to the input of the neural network. The expectation is that near-perfect mine detection will be possible with this proposed system.

Paper Details

Date Published: 31 May 1996
PDF: 12 pages
Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); doi: 10.1117/12.241242
Show Author Affiliations
Gregory Plett, Stanford Univ. (United States)
Takeshi Doi, Stanford Univ. (United States)
Don Torrieri, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 2765:
Detection and Remediation Technologies for Mines and Minelike Targets
Abinash C. Dubey; Robert L. Barnard; Colin J. Lowe; John E. McFee, Editor(s)

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