Fast data-derived fundamental spheroidal excitation models with application to UXO identification

Current idealized forward models for electromagnetic induction (EMI) response can be defeated by the characteristic material and geometrical heterogeneity of realistic unexploded ordnance (UXO). A new, physically complete modeling system includes all effects of these heterogeneities and their interactions within the object, in both near and far fields. The model is fast enough for implementation in inversion processing algorithms. A method is demonstrated for deriving the model parameters from straight forward processing of training data from a defined measurement protocol. Depending on the EMI sensor used for measurements, the process of inferring model parameters is more or less ill-posed. More complete data can alleviate the problem. For a given set of training data, special numerical treatment is introduced to take the best advantage of the data and obtain reliable model parameters. This fast model is implemented in a "fingerprint" testing approach in which two different UXOs are identified from the measurement data. Preliminary results showed that this fast model is promising for UXO identification.