Physics-model-based unexploded ordnance discrimination using wideband EMI data

Unexploded ordnance (UXO) discrimination is investigated using the wide band electromagnetic induction (EMI) data. The main focus of this paper is on the practical phenomenological modeling for the induced wideband EMI sensor response from different targets. Modeling for the sensor response provides feature vectors to UXO classification algorithms, and it has been proven to be very important for the improvement of the overall remediation performance. A parametric model is discussed with the emphsis on multiple offset dipole centers. The measured data from several actual targets are utilized to validate the model and to demonstrate the advantage of multiple offset dipole centers vs. single dipole center. We further illustrate the application of the model with multiple dipoles in target classifications by numerical examples. We show that the classification performance might be improved substantially. Finally, we state that the nonlinear EMI dipole model can be decomposed into a linear model. Thus it benefits from the rich literature of linear algebra and signal processing. To report one of our efforts, two methods are proposed to detect the number of dipoles blindly by the information theoretic criteria, namely the Akaike information criterion (AIC) and the minimum description length (MDL). The methods are testified using measured EMI data.