A comparison of algorithms for subsurface target detection and identification using time-domain electromagnetic induction data

In this paper, the performance of subsurface target identification algorith ms using data from time-domain electro- magnetic induction (EMI) sensors is investigated. The response of time-domain EMI sensors to the presence of a conducting object may be modeled as a weighted sum of decaying exponential signals. Although the weights associated with each of the modes are depend ent on the target/sensor orientation, the decay rates are a function of the target's composition and geometry and therefore are intrinsic to the targe t. Since the decay rates are not dependent on target/sensor orientation or other unobservable-parameters, decay rate estimation has previously been pr oposed as a viable method for target identification, The performance attain ed with Bayesian target identification algorithms operating on the entire t ime-domain signal and decay rate estimates is compared through both numeric al simulations and application to experimental data. The decay rate estimat es utilized in the numerical simulations are assumed to achieve the Cramer- Rao lower bound (CRLB), which provides a lower bound on the variance of an unbiased parameter estimate. The simulations as well as results obtained wi th experimental data show that processing the entire time-domain signal pro vides better target identification and discrimination performance than proc essing decay rate estimates.