Using graph clustering to locate sources within a dense sensor array

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• 作者：Nima Riahi ; ^{nriahi@ucsd.edu" class="auth_mail" title="E-mail the corresponding author} ; Peter Gerstoft ^{gerstoft@ucsd.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Seismic arrays ; Source localization ; Graph clustering ; Non-parametric estimation
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：132
• 期：Complete
• 页码：110-120
• 全文大小：2609 K

文摘

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 levels 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 a 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 $7\mathrm{km}\times 10\mathrm{km}$ of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.