Nonparametric one-bit quantizers for distributed estimation

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

2 Scopus citations

Abstract

In this paper, we consider the distributed parameter estimation problem using one-bit quantized data from local sensors. Nonparametric distributed estimators are proposed based on knowledge of the moments of sensor noise. These estimators are shown to be either unbiased or asymptotically unbiased with bounded estimation variance for all possible parameter values. Relationship between the proposed approaches and dithering in quantization is investigated. Performance comparison is made between the proposed estimators and the Sign quantizer via an illustrative example.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Information Theory - Proceedings
Pages459-463
Number of pages5
DOIs
StatePublished - 2008
Event2008 IEEE International Symposium on Information Theory, ISIT 2008 - Toronto, ON, Canada
Duration: Jul 6 2008Jul 11 2008

Other

Other2008 IEEE International Symposium on Information Theory, ISIT 2008
CountryCanada
CityToronto, ON
Period7/6/087/11/08

    Fingerprint

Keywords

  • Distributed estimation
  • Nonparametric distributed estimation
  • Sensor networks

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Theoretical Computer Science
  • Information Systems

Cite this

Chen, H., & Varshney, P. K. (2008). Nonparametric one-bit quantizers for distributed estimation. In IEEE International Symposium on Information Theory - Proceedings (pp. 459-463). [4595028] https://doi.org/10.1109/ISIT.2008.4595028