Minimal energy decentralized estimation based on sensor noise variance statistics

Jwo-Yuh Wu*, Qian Zhi Huang, Ta-Sung Lee

*Corresponding author for this work

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

1 Scopus citations

Abstract

This paper studies minimal-energy decentralized estimation in sensor networks under best-linear-unbiased-estimator fusion rule. While most of the existing related works require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy is subject to certain performance constraint in terms of mean square error (MSE) averaged over the noise variance distribution. A closed-form formula for the overall MSE metric is derived, based on which the problem can be reformulated in the form of convex optimization and is shown to yield an analytic solution. The proposed method shares several attractive features of the existing designs via instantaneous noise variances; through simulations it is seen to significantly improve the energy efficiency against the uniform allocation scheme.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - 6 Aug 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period15/04/0720/04/07

Keywords

  • Convex optimization
  • Decentralized estimation
  • Energy minimization
  • Quantization
  • Sensor networks

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  • Cite this

    Wu, J-Y., Huang, Q. Z., & Lee, T-S. (2007). Minimal energy decentralized estimation based on sensor noise variance statistics. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 [4217580] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). https://doi.org/10.1109/ICASSP.2007.366407