Present and future methods of mine detection using scattering parameters and an artificial neural network

Gregory Plett; Takeshi Doi; Don Torrieri

doi:10.1117/12.241242

31 May 1996 Present and future methods of mine detection using scattering parameters and an artificial neural network

Gregory Plett, Takeshi Doi, Don Torrieri

Proceedings Volume 2765, Detection and Remediation Technologies for Mines and Minelike Targets; (1996) https://doi.org/10.1117/12.241242
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Gregory Plett, Takeshi Doi, and Don Torrieri "Present and future methods of mine detection using scattering parameters and an artificial neural network", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241242

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