Analysis of EMI scattering to support UXO discrimination: heterogeneous and multiple objects

Abstract

Near field ( ~ 1 m) electromagnetic induction (EMI) sensing, from 10's of Hz up to 100's of kHz, has been successful in detecting subsurface metallic targets. However, the discrimination of buried unexploded ordinance (UXO) from innocuous objects still remains a challenging problem. The EM fields radiated by both antenna and target fall off very sharply as function ~1/R 3 , for a combined decay rate of ~ 1/R 6 . Therefore EMI sensors affect different materials and sections of the target differently [1-2], and signals depend very strongly on what parts of the target are closest to the sensor. Taking into account proximity effects is particularly important for identification and discrimination of actual UXO. The classification of unseen, buried objects, which in general is an inverse problem, requires very fast and accurate representation of the target response. To address these critical issues and to enhance of UXO identification, this paper presents very fast, rigorous ways to compute EMI scattering from a composite target. The method is based on the hybrid full method of auxiliary source (MAS) [1] and MAS-thin skin depth approximation technique (MAS-TSA) [3], together with modal decomposition and reduced source set techniques. For general excitation, a primary field is decomposed into the fundamental spheroidal modes on a fictious spheroid surrounding a real target. Then the problem is solved for each spheroidal mode, taking advantage of axial symmetry. Finally the total response from the target is reproduced using only a few auxiliary magnetic charges. The numerical results are given and compared with experimental data.

A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward Models and Statistical Signal Processing

“…The expansion coefficient  m thus corresponds to the NSMS amplitude at the m-th belt. Substituting into (29) we obtain…”

Isolating and Discriminating Overlapping Signatures in Cluttered Environments

“…The BLU26 exhibits similar characteristics but the three polarizabilities are closer to each other due to the close to spherical shape of this UXO (see inset). 4 Table 3: Inverted positions of single targets using data from the MPV instrument. The time signatures for the 81-mm and the BLU26 are shown in Figure 5.…”

“…Response from shallow yet small metallic clutter can often obscure deeper targets of interest (for example, see Figs. [28][29][30], and the electromagnetic induction (EMI) response from targets in close proximity may affect each other due to mutual coupling [3,4] and confuse or confound inversion schemes. These realities often lead to misclassification at best, or a complete failure of the routine at worst.…”

“…Recently, several EMI sensing and data-inversion techniques [4,20,24,[50][51][52][53][54][55][56][57][58][59][60][61][62][63][64] have been developed for detecting and discriminating between UXO and non-UXO items. Typically the first step of these methods is the recovery of a set of parameters that specify a physics-based model representing the object under interrogation.…”

“…These algorithms are carried out by determining an objective function as a goodness-of-fit measure between the forward model and measurement data. Although various forward EMI models (such as single-dipole, multi-dipole [20,[50][51][52][53], and normalized surface magnetic source [4,24]) have demonstrated great UXO discrimination capabilities for well-isolated single targets using data provided by next-generation EMI sensors, single-target discrimination capability is considerably limited for overlapping EMI signals; i.e., when a sensor detects EMI signals from two or more targets at the same time. The obvious extension of current EMI modeling to accommodate overlapping EMI signatures is multipletarget modeling.…”