Decentralized data selection for MAP estimation: A censoring and quantization approach

Eric J. Msechu, Georgios B Giannakis

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

6 Scopus citations

Abstract

A distributed data selection technique for fusion center (FC)-based estimation with a wireless sensor network (WSN) is presented. The data selection is envisioned for a large WSN in which only a subset of measurements need be transmitted to the FC thereby saving on transmission power. Furthermore, quantization of the selected measurements leading to bandwidth savings is also addressed. A novel data selection method using measurement censoring is followed by maximum a posteriori estimation that optimally fuses information from the censored-data model. Censoring and estimation algorithms that are amenable to implementation with WSNs are developed. Bayesian Cramér-Rao bound analysis and numerical simulations show that the proposed censoring-based estimator and quantized-censored estimator have competitive (or even superior) mean-square error performance when compared to data selection alternatives under a range of sensing conditions.

Original languageEnglish (US)
Title of host publicationFusion 2011 - 14th International Conference on Information Fusion
StatePublished - Sep 13 2011
Event14th International Conference on Information Fusion, Fusion 2011 - Chicago, IL, United States
Duration: Jul 5 2011Jul 8 2011

Publication series

NameFusion 2011 - 14th International Conference on Information Fusion

Conference

Conference14th International Conference on Information Fusion, Fusion 2011
CountryUnited States
CityChicago, IL
Period7/5/117/8/11

Keywords

  • Censoring
  • Data reduction
  • MAP estimation
  • Quantization
  • Sensor selection

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