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

Author

Azimi-Sadjadi, Mahmood R. ; Poole, David E. ; Sheedvash, Sassan ; Sherbondy, Kelly D. ; Stricke, Scott A.

Author_Institution

Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA

Volume

41

Issue

1

fYear

1992

fDate

2/1/1992 12:00:00 AM

Firstpage

137

Lastpage

143

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

Keywords

data reduction; dielectric measurement; microwave detectors; neural nets; pattern recognition; signal processing; buried dielectric anomalies; classification; clutter; correlation; data reduction; data representation; detection; landmines; neural network discriminator; noise; parameter estimation; separated aperture microwave sensor; targets; trainability; training; Acoustic sensors; Apertures; Artificial neural networks; Dielectrics; Landmine detection; Microwave sensors; Neural networks; Object detection; Optoelectronic and photonic sensors; Sensor systems;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.126648

Filename

126648