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Proceedings Paper

Correlation-based land mine detection using GPR
Author(s): King C. Ho; Paul D. Gader
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Paper Abstract

This paper proposes the use of a linear prediction technique in the frequency domain for landmine detection. A clutter vector sample is modeled by a linear prediction model, where the current clutter vector sample can be expressed as a linear combination of the past few vector samples plus a random component. The detector first computes the Maximum Likelihood estimate of the prediction coefficients and then generates the prediction error. The detector decides the current sample is from landmine if the prediction error is large. Subband processing is also proposed to further improve the performance of the detector. Detection results are provided on measured data collected from a variety of geographical locations. The data sets contain over 2300 mine encounters.

Paper Details

Date Published: 22 August 2000
PDF: 8 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396194
Show Author Affiliations
King C. Ho, Univ. of Missouri/Columbia (United States)
Paul D. Gader, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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