Source separation using sparse-solution linear solvers

Jonathan T. Miller; Dean Keiswetter; Jim Kingdon; Tom Furuya; Bruce Barrow; Tom Bell

doi:10.1117/12.850412

29 April 2010 Source separation using sparse-solution linear solvers

Jonathan T. Miller, Dean Keiswetter, Jim Kingdon, Tom Furuya, Bruce Barrow, Tom Bell

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 766409 (2010) https://doi.org/10.1117/12.850412
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

An algorithm is proposed to enumerate, locate and characterize individual signal sources given observation of their combined signals. No a-priori estimate for the number of sources is required. We assume a forward model exists, and that superposition holds, i.e. coupling between sources is ignored. A system of linear equations y=Ax is set up in which columns of matrix A contain expected signals from a large number of hypothesized sources, and y contains the observed signal. Recently-developed solvers designed for linear systems with sparse non-negative solutions make this approach feasible even when large numbers of sources are involved. With each iteration, the collection of hypothesized sources is refined using a Harmony Search algorithm. Application is demonstrated on the problem of locating multiple buried conductors based on electromagnetic induction (EMI) signals observed at ground surface.

Citation Download Citation

Jonathan T. Miller, Dean Keiswetter, Jim Kingdon, Tom Furuya, Bruce Barrow, and Tom Bell "Source separation using sparse-solution linear solvers", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766409 (29 April 2010); https://doi.org/10.1117/12.850412

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