Name
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 x 10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array. |
Year | Venue | Field |
|---|---|---|
2017 | European Signal Processing Conference | Test statistic,Geophone,Matrix (mathematics),Sensor array,Matrix decomposition,Algorithm,Coherence (physics),Artificial intelligence,Covariance matrix,Clustering coefficient,Machine learning,Mathematics |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nima Riahi | 1 | 2 | 0.79 |
| Peter Gerstoft | 2 | 86 | 22.34 |
| Christoph F. Mecklenbrauker | 3 | 162 | 20.69 |