Using graph clustering to locate sources within a dense sensor array

TitleUsing graph clustering to locate sources within a dense sensor array
Publication TypeJournal Article
Year of Publication2017
AuthorsRiahi N., Gerstoft P
JournalSignal Processing
Volume132
Pagination110-120
Date Published2017/03
Type of ArticleArticle
ISBN Number0165-1684
Accession NumberWOS:000389294600011
Keywordsboundary; california; estimation; field; Graph clustering; long beach; noise; Non-parametric; Seismic arrays; source localization
Abstract

We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering No knowledge about the propagation medium is needed except that signal strengths decay to insignificant level! within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well separated sources induce clusters in this graph. The geographic spread of these clusters can serve to localize the. sources. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with robust phase-only coherence test statistic combined with a physical distance criterion. The latter criterion ensures graph sparsity and thus prevents clusters from forming by chance. We verify the approach and quantify its reliability on a simulated dataset. The method is then applied to data from a dense 5200 element geophone array that blanketed 7 km x 10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.

DOI10.1016/j.sigpro.2016.10.001
Student Publication: 
No
Research Topics: 
sharknado