Mine detection using scattering parameters

Author

Plett, Gregory L. ; Doi, Takeshi ; Torrieri, Don

Author_Institution

Dept. of Electr. Eng., Stanford Univ., CA, USA

Volume

8

Issue

6

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

1456

Lastpage

1467

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

Keywords

eigenvalues and eigenfunctions; feature extraction; feedforward neural nets; microwave detectors; object detection; pattern classification; transforms; weapons; antipersonnel land mines; artificial neural network pattern classifier; data-specific preprocessing methods; eigenspace separation transform; feedforward neural network; generalized Karhunen-Loeve transform; ground conflict; mine detection; neural network learning; scattering parameters; separated-aperture microwave sensor; Apertures; Artificial neural networks; Costs; Detectors; Face detection; Landmine detection; Microwave sensors; Scattering parameters; Sensor phenomena and characterization; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.641468

Filename

641468