Sensor Fusion for Hand-held Multi-Sensor Landmine Detection

Abstract

Sensor fusion issues in a streamlined assimilation of multi-sensor information for landmine detection are discussed. In particular multi-sensor fusion in hand-held landmine detection system with ground penetrating radar (GPR) and metal detector sensors is investigated. The fusion architecture consists of feature extraction for individual sensors followed by a feed-forward neural network training to learn the feature space representation of the mine/no-mine classification. A correlation features from GPR, and slope and energy features from metal detector are used for discrimination. Various fusion strategies are discussed and results compared against each other and against individual. Both feature level and decision level fusion have been investigated. Simple decision level fusion schemes based on Dempster-Shafer evidence accumulation, soft AND, MIN and MAX are compared. Feature level fusion using neural network training is shown to provide best results. However comparable performance is often achieved using decision fusion based on Dempster-Shafer theory. It is noted that the simple fusion scheme lacks a means to verify detections after a decision has been made. New detection algorithms that are more than anomaly detectors are needed. Preliminary results with features based on independent component analysis (ICA) show promising results towards this end. An improved fusion scheme using ICA features is proposed.

Meeting Name

SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI (2001: Apr. 16, Orlando, FL)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Feature Extraction; Feedforward Neural Networks; Ground Penetrating Radar Systems; Independent Component Analysis; Learning Systems; Metal Detectors; Principal Component Analysis; Sensor Data Fusion; Landmine Detection; Mining Engineering; Dempster-shafer Theory; Ground Penetrating Radar; Landmine-detection; Metal Detector; Multi-sensor Fusion; Neural-networks

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2001 SPIE -- The International Society for Optical Engineering, All rights reserved.

Publication Date

01 Apr 2001

Link to Full Text

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