A low complexity estimation architecture based on noisy comparators

Ryan M. Corey, Andrew C. Singer, Sen Tao, Naveen Verma

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

Abstract

We consider a low-complexity architecture for scalar estimation using unreliable observations. A signal is observed using a number of binary comparisons for which the threshold levels can vary randomly. We analyze the statistics of this system and find a Cramér-Rao lower bound on the squared error performance of the estimator. By incorporating redundant observations and applying statistical estimation techniques, we form an estimate with error that is much smaller than the uncertainty in the threshold levels. We propose a two-stage architecture that achieves near-optimal mean square estimation error with low complexity. The performance of the architecture is evaluated using a simulated prototype.

Original languageEnglish (US)
Title of host publicationIEEE Workshop on Signal Processing Systems, SiPS
Subtitle of host publicationDesign and Implementation
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479965885
DOIs
StatePublished - Dec 15 2014
Event2014 IEEE Workshop on Signal Processing Systems, SiPS 2014 - Belfast, United Kingdom
Duration: Oct 20 2014Oct 22 2014

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130

Other

Other2014 IEEE Workshop on Signal Processing Systems, SiPS 2014
CountryUnited Kingdom
CityBelfast
Period10/20/1410/22/14

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

Keywords

  • Distributed estimation
  • parameter estimation
  • quantization
  • sensor networks

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  • Cite this

    Corey, R. M., Singer, A. C., Tao, S., & Verma, N. (2014). A low complexity estimation architecture based on noisy comparators. In IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation [6986081] (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SiPS.2014.6986081