Abstract
AbstractThis paper concerns the problem of large-scale source localization arising when many potential sources must be classified as either active or inactive such that the probability of missing an active source is bounded. A new iterative heuristic called the Iterative Source Localization Procedure (ISLoP) is introduced that reduces the complexity of a source localization problem with J potential sources from 2J to J per iteration, while also providing a local bounds on the maximum probability of a missed source. The ISLoP separates the source localization problem into a likelihood maximization problem followed by an active source localization problem. A diffusion example is used to demonstrate the performance of the ISLoP when compared to an estimation-based approach, where the heuristic is shown to have increasingly better performance as the bound on the maximum probability of a missed source is decreased. An experimental evaluation of the heuristic with respect to common wireless sensor networking errors is provided using a test bed implementation for a CO2 sequestration site monitoring problem.
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