Bayesian optimal classification of metallic objects: a comparison of time-domain and frequency-domain EMI performance

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 response, 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 filter that is matched to the signature of the object at a particular (known) depth. Furthermore, in the real 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.