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Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator

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dc.contributor.authorPoole, David E., author
dc.contributor.authorSherbondy, Kelly D., author
dc.contributor.authorSheedvash, Sassan, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorStricker, Scott A., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:36Z
dc.date.available2007-01-03T04:43:36Z
dc.date.issued1992
dc.description.abstractThe 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.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationAzimi-Sadjadi, Mahmood R., Detection and Classification of Buried Dielectric Anomalies Using a Separated Aperture Sensor and a Neural Network Discriminator, IEEE Transactions on Instrumentation and Measurement 41, no. 1 (February 1992): 137-143.
dc.identifier.urihttp://hdl.handle.net/10217/851
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1992 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdata reduction
dc.subjectdielectric measurement
dc.subjectmicrowave detectors
dc.subjectneural nets
dc.subjectpattern recognition
dc.subjectsignal processing
dc.titleDetection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator
dc.typeText

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