Source separation using sparse-solution linear solvers

Miller, James D.; Keiswetter, Dean; Kingdon, J. B.; Furuya, Tom; Barrow, Bruce; Bell, Tom

doi:10.1117/12.850412

Cited by 4 publications

(4 citation statements)

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“…The work presented in (Miller et al, 2010) reports promising results in this direction found using harmony search, a method in which "trials" with a preset number of sources are sequentially generated -either afresh or by making slight changes to existing configurations -and accepted or discarded depending on their "success" -i.e., the closeness of their predictions to measured values. The authors manage to properly resolve multi-target data taken with TEMTADS at the Blossom Point Army Research Facility in Maryland and at APG.…”

Section: Introductionmentioning

confidence: 93%

“…The method we propose here is fundamentally different in that it is not stochastic. As in (Miller et al, 2010), we (a) seed the region below the surface with a distribution of point dipoles, each of which contributes to the measured magnetic field, and (b) iteratively update the dipoles' polarizabilities -that is, their dipole moments but dividing out the primary field that induces them-so as to minimize the mismatch between computed and measured fields over a grid, but here the similarities end. Instead of looking for the minimum sufficient number of sources, we look for the sources that have the most influence on the measured data and iteratively "zoom in" on them.…”

Section: Introductionmentioning

confidence: 99%

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Subsurface electromagnetic induction imaging for unexploded ordnance detection

Grzegorczyk¹,

Fernández

Shubitidze

et al. 2012

Journal of Applied Geophysics

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Subsurface electromagnetic induction imaging for unexploded ordnance detection

Grzegorczyk¹,

Fernández

Shubitidze

et al. 2012

Journal of Applied Geophysics

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“…Our discrimination approach uses a model-based estimation procedure to determine whether or not an unknown target is likely to be a UXO item. It entails estimating the size and shape of the target from the spatial pattern of the induced field above the target [3,4,5]. The EMI signal is a linear function of the flux through the receiving coil.…”

Section: Bparameter Estimationmentioning

confidence: 99%

2010 ESTCP UXO Classification Study, Camp Butner, NC

Keiswetter¹

2012

Self Cite

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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%

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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Source separation using sparse-solution linear solvers

Cited by 4 publications

References 0 publications

Subsurface electromagnetic induction imaging for unexploded ordnance detection

Subsurface electromagnetic induction imaging for unexploded ordnance detection

2010 ESTCP UXO Classification Study, Camp Butner, NC

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

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