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Proceedings Paper

Modified Kalman target detection algorithm applied to metal detection
Author(s): Canicious Abeynayake; Ian J. Chant; Graeme Nash
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Paper Abstract

We discuss an improved Kalman filter-based algorithm for automatic detection of targets from metal detector data. This innovations process utilizes the difference between measurements and single-stage predicted values. In our previous work a Kalman filter based algorithm was used to detect targets assuming that the metal detector output signal is a constant in the background. In this work we extend the capability of this method to detect targets by assuming the distribution of the metal detector output data is Gaussian. The analysis has been extended by computing state estimation errors, covariance matrices and treating metal detector background data as a discrete-time Gauss-Markov random sequence. The proposed detection algorithms have been applied to Minelab F1A4-MIM metal detector data.

Paper Details

Date Published: 13 August 2002
PDF: 11 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479156
Show Author Affiliations
Canicious Abeynayake, Defence Science and Technology Organisation (Australia)
Ian J. Chant, Defence Science and Technology Organisation (Australia)
Graeme Nash, Defence Science and Technology Organization (Australia)

Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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