Estimation in Poisson Noise: Properties of the Conditional Mean Estimator

Alex Dytso, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Lévy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero.

Original languageEnglish (US)
Article number9032131
Pages (from-to)4304-4323
Number of pages20
JournalIEEE Transactions on Information Theory
Volume66
Issue number7
DOIs
StatePublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Keywords

  • Gaussian noise
  • Lévy distance
  • Poisson noise
  • conditional mean
  • empirical Bayes
  • estimation theory
  • linear estimation

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