Distributed estimation using reduced dimensionality sensor observations

Ioannix D. Schizas, Georgia B. Giannakis, Zhi Quan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We deal with linear estimation of random signals based on reduced-dimensionality observations collected at distributed sensors and communicated to a fusion center through wireless links. Dimensionality reduction compresses sensor data to meet low-power and bandwidth constraints, while linearity in compression and estimation are well motivated by the limited computing capabilities wireless sensor networks are envisioned to operate with. We cast this intertwined compression-estimation problem in a canonical correlation analysis framework, and derive closed-form along with coordinate descent estimators which guarantee convergence at least to a stationary point. Performance analysis and corroborating simulations demonstrate the merits of the novel distributed estimators relative to existing alternatives.

Original languageEnglish (US)
Title of host publicationConference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers
Pages1029-1033
Number of pages5
StatePublished - Dec 1 2005
Event39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Oct 28 2005Nov 1 2005

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2005
ISSN (Print)1058-6393

Other

Other39th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period10/28/0511/1/05

Fingerprint Dive into the research topics of 'Distributed estimation using reduced dimensionality sensor observations'. Together they form a unique fingerprint.

Cite this