Land Mine and Clutter Object Discrimination using Wavelet and Time Domain Spatially Distributed Features from Metal Detectors and their Fusion with GPR Features for Hand-Held Units

Abstract

This paper presents some advances in discrimination and fusion algorithms using metal detector (MD) and ground penetrating radar (GPR) sensors in a robotic wand unit. Previously investigated spatially distributed features are extended and fused with discrete wavelet transform representations of MD data. A multilayer perceptron technique is then applied to discriminate between land mine and land mine-like objects based on the wavelet coefficient and time domain features separately. Using MD wavelet and time domain fusion, the probability of false alarms is reduced by 46.0% and 18.0% over the wavelet and time domain models, respectively, at 0.95 probability of detection. Fusion results are presented for the MD and GPR sensors to demonstrate that the two sensors provide complementary information for significantly reducing the probability of false alarm. Blind test results from a government test facility are presented to evaluate the effectiveness of the algorithms.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

CyTerra
United States. Army

Keywords and Phrases

Algorithms; Discrete Wavelet Transforms; Ground Penetrating Radar Systems; Metal Detectors; Multilayer Neural Networks; Probability; Time Domain Analysis; Clutter Object Discrimination; Land Mine; Robotic Wand Unit; Bombs (Ordnance); Electromagnetic induction; Land mines -- Detection; Metal detectors

International Standard Serial Number (ISSN)

0278-081X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2007 Birkhäuser Verlag, All rights reserved.

Publication Date

01 Apr 2007

Link to Full Text

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