Experimental Demonstration of an Autonomous Distributed-Consensus Network for Underwater Passive-Acoustic Detection

Abstract

In this article an autonomous distributed-consensus system is evaluated with regard to its efficacy for establishing a common detection statistic among a network of passive acoustic hydrophones. The notion of consensus among sensors is briefly reviewed, and an algorithm motivated by diffusion of information among network neighbors is developed within the context of spectral graph theory. Relative entropy derived at each node provides information that the network averages to reach a joint detection decision. As the expected value of a log-likelihood ratio, relative entropy is the appropriate detection statistic for this application because it is nondimensional, additive, and insensitive to calibration. These properties facilitate the fusion of information among heterogeneous sensors. Seven nodes of an acoustical receiver network were moored on the New Jersey coastal shelf in May 2019, while towed and autonomous sources provided target signals at 900–1050 Hz. The convergence rate of the system to a detection statistic was found to be limited by slow acoustic communications, and convergence to a global consensus was often disrupted by communication breakdowns. When failures occurred, the network successfully demonstrated robustness by dissociating into viable subnetworks, each of which converged to a valid detection statistic.