Reprinted From: Proc. Spie V.2765, \detection and Remediation Technologies for Mines and Minelike Present and Future Methods of Mine Detection Using Scattering Parameters and an Artiicial Neural Network

The detection and disposal of anti-personnel landmines is one of the most diicult and intractable problems faced in ground connict. This paper rst presents current detection methods which use a separated aperture microwave sensor and an artiicial neural-network pattern classiier. Several data-speciic pre-processing methods are developed to enhance neural-network learning. In addition, a generalized Karhunen-Lo eve 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.