Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator
Date
1992
Authors
Poole, David E., author
Sherbondy, Kelly D., author
Sheedvash, Sassan, author
Azimi-Sadjadi, Mahmood R., author
Stricker, Scott A., author
IEEE, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods.
Description
Rights Access
Subject
data reduction
dielectric measurement
microwave detectors
neural nets
pattern recognition
signal processing