A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations

Hao Chen

doi:10.1109/SAM.2012.6250459

A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations

Hao Chen

Electrical Engineering Department

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

1 Scopus citations

Abstract

In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.

Original language	American English
Title of host publication	IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	177-180
Number of pages	4
ISBN (Print)	9781467310710
DOIs	https://doi.org/10.1109/SAM.2012.6250459
State	Published - 2012
Event	2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 - Hoboken, NJ, United States Duration: 17 Jun 2012 → 20 Jun 2012

Publication series

Name	Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)	2151-870X

Conference

Conference	2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Country/Territory	United States
City	Hoboken, NJ
Period	17/06/12 → 20/06/12

Keywords

Dependent Observations
Distributed Detection
Likelihood Quantizer

EGS Disciplines

Electrical and Computer Engineering

Access to Document

10.1109/SAM.2012.6250459

Cite this

Chen, H. (2012). A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations. In IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 177-180). Article 6250459 (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAM.2012.6250459

@inproceedings{a304768b87f449038cf5f40cd681bc15,

title = "A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations",

abstract = " In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.",

keywords = "Dependent Observations, Distributed Detection, Likelihood Quantizer",

author = "Hao Chen",

year = "2012",

doi = "10.1109/SAM.2012.6250459",

language = "American English",

isbn = "9781467310710",

series = "Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "177--180",

booktitle = "IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)",

address = "United States",

note = "2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 ; Conference date: 17-06-2012 Through 20-06-2012",

}

Chen, H 2012, A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations. in IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)., 6250459, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, Institute of Electrical and Electronics Engineers Inc., pp. 177-180, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012, Hoboken, NJ, United States, 17/06/12. https://doi.org/10.1109/SAM.2012.6250459

A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations. / Chen, Hao.
IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM). Institute of Electrical and Electronics Engineers Inc., 2012. p. 177-180 6250459 (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop).

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

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AU - Chen, Hao

PY - 2012

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N2 - In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.

AB - In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.

KW - Dependent Observations

KW - Distributed Detection

KW - Likelihood Quantizer

UR - https://www.scopus.com/pages/publications/84867220550

UR - https://scholarworks.boisestate.edu/electrical_facpubs/204

U2 - 10.1109/SAM.2012.6250459

DO - 10.1109/SAM.2012.6250459

M3 - Conference contribution

SN - 9781467310710

T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop

SP - 177

EP - 180

BT - IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012

Y2 - 17 June 2012 through 20 June 2012

ER -

A Hierarchical Model for Distributed Detection with Conditionally Dependent Observations

Abstract

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Keywords

EGS Disciplines

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