Adaptive and Iterative Processing Techniques for Overlapping Signatures

Bell, Tom

doi:10.21236/ada468558

Cited by 5 publications

(3 citation statements)

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“…To accurately extract polarization tensor parameters from the EMI response, we need reasonably accurate estimates of the location of the source. We also need to decide if the observed anomaly is due to a single object or two or more objects (Bell, 2006;Grzegorczyk et al, 2009;Song et al, 2009;Miller et al, 2010;Shubitidze et al, 2010;Song et al, 2011). Recent advances in EMI instrumentation have produced systems that are able to acquire multistatic/multicomponent data.…”

Section: Introductionmentioning

confidence: 99%

“…When using electromagnetics for UXO problems, the standard processing method of finding buried sources is to formulate a nonlinear data-fitting optimization problem that is solved for source location and polarizations of the UXO from an initial model (Pasion and Oldenburg, 2001;Smith and Morrison, 2004;Bell, 2006;Grzegorczyk et al, 2009;Song et al, 2009;Shubitidze et al, 2010;Song et al, 2011). However, the problem of finding source location can be completely separated from the estimation of polarization spectra.…”

Section: Introductionmentioning

confidence: 99%

“…This results in a richer and more coherent data matrix than could be obtained from monostatic equipment. As in the radar case (Devaney, 2005), we expect that by combining the multistatic response measurements and the MUSIC principle an improved technique might be obtained, compared to the standard one (Pasion and Oldenburg, 2001;Smith and Morrison, 2004;Bell, 2006;Grzegorczyk et al, 2009;Song et al, 2009;Shubitidze et al, 2010;Song et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Estimating source locations of unexploded ordnance using the multiple signal classification algorithm

Song

Oldenburg

Pasion³

2012

GEOPHYSICS

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To extract intrinsic polarization parameters of a buried object from electromagnetic induction (EMI) responses, one has to first find its location. We developed an efficient method to find the approximate locations of multiple ordnance items using time domain electromagnetic data. The procedure was based upon the principle of multiple signal classification which exploits the orthogonality of signal and noise subspaces of multistatic EMI data. For an arbitrary multistatic array, we formulated transmitter-based and receiver-based imaging or steering vectorial operators that related with the left and right singular vectors of a multistatic response matrix. The operators were computed at potential source locations and are mapped onto the noise subspaces derived from data. A spatial metric function was therefore introduced to measure the magnitude of the projection. A 3D source imaging of the metric function could be obtained by evaluating all potential locations over a region of interest. In ideal cases, the perfect orthogonality between the computed signal subspace and measured noisy subspace can be achieved at or near a target location, and rendered an imaging peak at that location. Conversely, the peak image locations obtained from this technique were used as the indicators for where targets were most likely present. The number of targets could be estimated from the rank of the data matrix, provided there were a sufficient number of transmitters and receivers. In some instances the locations of multiple targets were imaged directly, but the procedure was enhanced by stripping the effect of a larger or shallower target from the image. The technique was evaluated using the test-stand and field data, and compared with the standard nonlinear inversion. The results showed that it has potential capability to accurately localize sources in EMI sensing.

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