### Abstract

Multiple-channel detection is considered in the context of a sensor network where raw data are shared only by nodes that have a common edge in the network graph. Established multiple-channel detectors, such as those based on generalized coherence or multiple coherence, use pairwise measurements from every pair of sensors in the network and are thus directly applicable only to networks whose graphs are completely connected. An approach is introduced that uses a maximum-entropy technique to formulate surrogate values for missing measurements corresponding to pairs of nodes that do not share an edge in the network graph. The broader potential merit of maximum-entropy baselines in quantifying the value of information in sensor network applications is also noted.

Original language | English (US) |
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Title of host publication | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 |

Pages | 297-300 |

Number of pages | 4 |

DOIs | |

State | Published - 2012 |

Event | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States Duration: Aug 5 2012 → Aug 8 2012 |

### Other

Other | 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 |
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Country | United States |

City | Ann Arbor, MI |

Period | 8/5/12 → 8/8/12 |

### Fingerprint

### Keywords

- Generalized coherence
- Maximum entropy
- Multiple-channel detection
- Sensor networks
- Value of information

### ASJC Scopus subject areas

- Signal Processing

### Cite this

*2012 IEEE Statistical Signal Processing Workshop, SSP 2012*(pp. 297-300). [6319686] https://doi.org/10.1109/SSP.2012.6319686

**Maximum-entropy surrogation in network signal detection.** / Cochran, Douglas; Howard, S. D.; Moran, B.; Schmitt, H. A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2012 IEEE Statistical Signal Processing Workshop, SSP 2012.*, 6319686, pp. 297-300, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 8/5/12. https://doi.org/10.1109/SSP.2012.6319686

}

TY - GEN

T1 - Maximum-entropy surrogation in network signal detection

AU - Cochran, Douglas

AU - Howard, S. D.

AU - Moran, B.

AU - Schmitt, H. A.

PY - 2012

Y1 - 2012

N2 - Multiple-channel detection is considered in the context of a sensor network where raw data are shared only by nodes that have a common edge in the network graph. Established multiple-channel detectors, such as those based on generalized coherence or multiple coherence, use pairwise measurements from every pair of sensors in the network and are thus directly applicable only to networks whose graphs are completely connected. An approach is introduced that uses a maximum-entropy technique to formulate surrogate values for missing measurements corresponding to pairs of nodes that do not share an edge in the network graph. The broader potential merit of maximum-entropy baselines in quantifying the value of information in sensor network applications is also noted.

AB - Multiple-channel detection is considered in the context of a sensor network where raw data are shared only by nodes that have a common edge in the network graph. Established multiple-channel detectors, such as those based on generalized coherence or multiple coherence, use pairwise measurements from every pair of sensors in the network and are thus directly applicable only to networks whose graphs are completely connected. An approach is introduced that uses a maximum-entropy technique to formulate surrogate values for missing measurements corresponding to pairs of nodes that do not share an edge in the network graph. The broader potential merit of maximum-entropy baselines in quantifying the value of information in sensor network applications is also noted.

KW - Generalized coherence

KW - Maximum entropy

KW - Multiple-channel detection

KW - Sensor networks

KW - Value of information

UR - http://www.scopus.com/inward/record.url?scp=84868245608&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84868245608&partnerID=8YFLogxK

U2 - 10.1109/SSP.2012.6319686

DO - 10.1109/SSP.2012.6319686

M3 - Conference contribution

SN - 9781467301831

SP - 297

EP - 300

BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

ER -