In this paper we study the problem of dynamic optimization of ping schedule in an active sonar buoy network deployed to provide persistent surveillance of a littoral area through multistatic detection. The goal of ping scheduling is to dynamically determine when to ping and which ping source to engage in order to achieve the desirable detection performance. For applications where persistent surveillance is needed for an extended period of time, it is expected that the energy available at each ping source is limited relative to the required system lifetime. Hence efficient management of power consumption for pinging is important to support the required lifetime of the network while maintaining acceptable detection performance. Our approach to ping optimization is based on the application of approximate Partially Observable Markov Decision Process (POMDP) techniques such as the rollout algorithm. To enable a practical implementation of the policy rollout, we apply sampling-based techniques based on a simplified model that approximates the detailed multistatic model. Using high fidelity sonar simulations, we evaluate the performance of the proposed approach and compare it with the greedy technique in terms of detection performance and system lifetime.
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