Consensus Detection of a Narrowband Acoustic Source by a Distributed Sensor Network
Abstract
Probabilistic central 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 for joint detection via the summation of independent detection statistics across the nodes. Central fusion, though, is not robust to loss of the fusion center. Distributed detection mediated by a dynamic consensus algorithm offers an alternative for effecting joint detection. The present work investigates the application of dynamic consensus to the distributed detection of a narrowband, time-stationary underwater acoustic source. A class of linear consensus dynamical systems applicable to the detection problem is identified and proved to provide asymptotic agreement of the consensus state with the central-fusion detection statistic. Low-frequency narrowband detection performances of independent-node and consensus statistical signal processing in a shallow-water waveguide are compared in computations for horizontally distributed networks consisting of either 5 single-element nodes or 5 vertical-array nodes. Detection performance is quantified by receiver operating characteristic (ROC) curves and, more reductively, by the information divergence of the signal-present from the signal-absent probability density function of the consensus state.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 12, 2021
- Accession Number
- AD1156848
Entities
People
- Steven Finette
- Thomas J. Hayward
Organizations
- United States Naval Research Laboratory