On Stability of Linear Estimators in Poisson Noise

Alex Dytso, H. Vincent Poor

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

1 Scopus citations

Abstract

This paper considers estimation of a random variable in Poisson noise. Specifically, the main focus is to assess optimality and near optimality conditions for linear estimators.In the first part of the paper, it is shown that linear estimators are optimal if and only if the underlying prior is a gamma distribution and the dark current parameter is zero.In the second part of the paper, a stability analysis of linear estimators is undertaken. Specifically, it is shown that if an optimal estimator is close to a linear estimator in an Lp,p ≥1 distance, then the underlying prior distribution is approximately gamma in the Lévy metric and the Kolmogorov metric.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages670-674
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Dytso, A., & Poor, H. V. (2019). On Stability of Linear Estimators in Poisson Noise. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 670-674). [9048782] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF44664.2019.9048782