Recently different time domain (TD) electromagnetic induction sensors have been developed and tested for UXO detection and discrimination. These sensors produce well-located, multi-axis, high-density data. One of such sensors is the Man Portable Vector (MPV) TD sensor build by G&G., Inc., which has two 75-cm diameter transmitter loops and five tri-axial cubic receivers located around the transmitter coils. This sensor produces unprecedented high-fidelity complete vector data sets. To take advantage of these high-quality data, in this paper we adapt the normalized surface magnetic source (NSMS) model to the MPV. The NSMS is a very simple and robust technique for predicting the EMI responses of various objects. The technique is applicable to any combination of magnetic or electromagnetic induction data for any arbitrary homogeneous or heterogeneous 3-D object or set of objects. The NSMS approach uses magnetic dipoles distributed on a fictitious closed surface as responding sources for predicting objects' EMI responses. The amplitudes of the NSMS sources are determined from actual measured data, and at the end the total NSMS is used as a discriminator. Usually, discrimination between UXO and non-UXO items is processed by first recovering the buried object's location and orientation using standard non-linear minimization techniques; this is the most time consuming part of the UXO classification process. In order to avoid solving a traditional ill-posed inverse scattering problem, here we adapt to TD-MPV data a recently developed physics-based approach [1], called (HAP), to estimate a buried object's location and orientation. The approach assumes the target exhibits a dipolar response and uses only three global values: (1) the magnetic field vector H, (2) the vector potential A, and (3) the scalar magnetic potential at a point in space. Of these three global values only the flux of the H field is measurable by the MPV sensor. However, the vector and scalar magnetic potentials can be recovered from measured magnetic field data using a 21) NSMS approach. To demonstrate the applicability of the NSMS and HAP techniques we report the results of a blind-test analysis using multi-axis TD MPV data collected at the U.S. Army's ERDC UXO test site.
