Consensus detection of a Gaussian underwater acoustic source by a distributed sensor network

Abstract

Probabilistic data fusion is a well-established algorithmic approach to the detection of underwater acoustic sources by a distributed sensor network. Direct communication of all of the network nodes with a fusion center would provide optimal joint detection via summation of log-likelihood ratios (LLR’s) across the nodes. However, a network that relies on a single fusion center for data exfiltration is not robust to the loss of the fusion center. Distributed detection mediated by dynamic consensus algorithms, which have their roots in the broader system-theoretic discipline of dynamic state estimation, offers an alternative that is robust to node loss. The present work illustrates the application of dynamic consensus algorithms to the distributed detection of underwater acoustic sources, taking as an example the detection of a stationary, ergodic Gaussian source in a shallow-water waveguide by a network of vertical-array nodes. A general definition of consensus algorithms is followed by the construction of particular linear consensus dynamical systems that (1) provide asymptotic agreement of the time-averaged consensus LLR with the optimal central-fusion LLR; and (2) equalize the mean cross-node disagreements of the consensus LLR's. Performance of centralized and consensus detection is compared in simulations. [Work supported by ONR.]