Abstract
The detection and disposal of antipersonnel land mines is one of the most difficult and intractable problems faced in ground conflict. This paper presents detection methods which use a separated-aperture microwave sensor and an artificial neural network pattern classifier. Several data-specific preprocessing 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.
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