Matrix analysis
Stochastic Shortest Path Games
SIAM Journal on Control and Optimization
Introduction to algorithms
Detection of Signals in Noise
Sensor deployment strategy for target detection
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Multisensor Data Fusion
Sensor Fusion for Target Detection and Tracking
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Protecting with Sensor Networks: Attention and Response
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
Nonlinear Optimization
Hidden Markov Models and Dynamical Systems
Hidden Markov Models and Dynamical Systems
Chebyshev inequalities with law-invariant deviation measures
Probability in the Engineering and Informational Sciences
Bayesian data fusion for distributed target detection in sensor networks
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
SPAR: stochastic programming with adversarial recourse
Operations Research Letters
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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.