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Proceedings Paper

Bayesian optimal classification of metallic objects: a comparison of time-domain and frequency-domain EMI performance
Author(s): Ping Gao; Leslie M. Collins; Lawrence Carin
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Paper Abstract

Traditionally, field EMI sensors are operated in the time- domain. The time-domain (TD) EMI sensor usually is a pulsed system. It contains both a transmitting coil and a receiving coil. After transmitting an excitation pulse, which generates the primary field, the receiving coil records the secondary field in the late time. Since a TD EMI sensor measures only the late-time responses, the information contained in the early time response is lost thus limiting the types of objects that can be discriminated. Alternatively, EMI sensors can be operated in the frequency- domain (FD). In this case, the excitations are sinusoidal signals and the sensor measures the static response. The advantages and disadvantages of TD and FD EMI sensors are reviewed in this paper. For landmine and UXO detection, discrimination of targets of interest from clutter is required, since the cost of large false alarm rates is substantial amounts of money, labor and time. In order to discriminate targets from clutter, Bayesian optimal classifiers are derived. Traditional detectors for these applications only utilize the energy of the signal at the position under test or the output of a matched world scenario, the depth of the underground objects is uncertain. The optimal classifier that we utilize takes these uncertainties into account also. In this paper, we present classification performance for four metal objects using TD and FD EMI data. Experimental data were taken with the PSS- 12, a standard army issued metal detector, and the GEM-3, a prototype frequency-domain EMI sensor. Although the optimal classifier improves performance for both TD and FD data, FD classification rate are higher than those for TD systems. The theoretical basis for this result is explored.

Paper Details

Date Published: 22 August 2000
PDF: 11 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396251
Show Author Affiliations
Ping Gao, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)
Lawrence Carin, Duke Univ. (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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