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We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance,...
In this paper, we propose a novel myopic sensor scheduling methodology for tracking a target moving through a network of energy-constrained acoustic sensors. Specifically, we address the problem of activating the minimum-energy combination of sensors in a network that maintains a desired squared-error accuracy in the target's position estimate. We first formulate the scheduling problem as a binary...
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