Stochastic Optimization of Sensor Placement for Diver Detection

Anton Molyboha () and Michael Zabarankin ()
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Anton Molyboha: Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey 07030
Michael Zabarankin: Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey 07030

Operations Research, 2012, vol. 60, issue 2, 292-312

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 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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