Stochastic Optimization of Sensor Placement for Diver Detection
Anton Molyboha, Michael Zabarankin
Operations Research · 2012 · Volume 60 · Issue 2
Abstract
A comprehensive framework for diver detection by a hydrophone network in an urban harbor is presented. It includes a signal processing algorithm and a diver detection test and formulates optimal hydrophone placement as a two-stage stochastic optimization problem with respect to different scenarios of underwater noise. The signal processing algorithm identifies sound intensity peaks associated with diver breathing and outputs a diver number measuring the likelihood of diver presence, whereas the diver detection test aggregates the diver numbers obtained from the hydrophones in a linear statistic and optimizes the statistic's coefficients and a detection threshold for each noise scenario. The serial dependence of the diver numbers on a short time scale (several detection periods) is modeled by a hidden Markov chain, and finding the worst-case diver's trajectory for each hydrophone placement and noise scenario is reduced to a linear programming problem. The framework is tested in numerical experiments with real-life data for circular and elliptic hydrophone placements and is shown to be superior to a deterministic energy-based approach.
Keywords
cost effectiveness, defense systems, surveillance, stochastic programming, statistical pattern analysis
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