@InProceedings{8081493,
  author = {N. Riahi and P. Gerstoft and C. F. Mecklenbräuker},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Graph clustering for localization within a sensor array},
  year = {2017},
  pages = {1669-1673},
  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 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 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 method is applied to a dense 5200 element geophone array that blanketed 7 km × 10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array.},
  keywords = {covariance matrices;graph theory;pattern clustering;seismometers;sensor arrays;statistical testing;graph clustering;model-free technique;propagation medium;spatial coherence matrix;dense 5200 element geophone array;signal strength decay;covariance matrix estimation;sensor array localization;weak source identification;wave field matrix;connectivity graph matrix;vertices;well-separated source clustering;robust phase-only coherence test statistics;physical distance criterion;Long Beach city;CA;helicopter;Coherence;Sensor arrays;Probability density function;Array signal processing;Matrix decomposition;Europe},
  doi = {10.23919/EUSIPCO.2017.8081493},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343237.pdf},
}

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