Detecting Landmines Using Weighted Density Distribution Function Features

Abstract

Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in land mines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. This research introduces new spatially distributed MD features for differentiating land mine signatures from background. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions.

Meeting Name

SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X (2001: Apr. 16, Orlando, FL)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Backpropagation; Metal Detectors; Neural Networks; Probability Density Function; Time Domain Analysis; Landmine Detections; Ground Penetrating Radar Systems; Electromagnetic Induction; Ground Penetrating Radar; Land Mine Detection; Metal Detector

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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