Bayesian cyclic bounds for periodic parameter estimation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In many practical periodic parameter estimation problems, the appropriate cost function is periodic with respect to the unknown parameter. In this paper a new class of cyclic Bayesian lower bounds on the mean cyclic error (MCE) is developed. The new class includes the cyclic version of the Bayesian Cramér-Rao bound (BCRB). The cyclic BCRB requires milder regularity conditions compared to the conventional BCRB. The tightest bound in the proposed class is derived and it is shown that under a certain condition it achieves the minimum MCE (MMCE). The new lower bounds are compared with the cyclic version of the Ziv-Zakai lower bound (ZZLB) and the MCE's of the MMCE and maximum aposteriori probability (MAP) estimators for frequency estimation with uniform a-priori probability density function (pdf) of the unknown parameter. In this common estimation problem, the conventional BCRB does not exist, while the proposed cyclic BCRB provides a valid lower bound for parameter estimation.

Original languageEnglish
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages308-311
Number of pages4
DOIs
StatePublished - 1 Dec 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: 15 Dec 201318 Dec 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Conference

Conference2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Country/TerritoryFrance
CitySaint Martin
Period15/12/1318/12/13

Keywords

  • Bayesian parameter estimation
  • cyclic Bayesian Cramér-Rao bound
  • cyclic performance bounds
  • periodic parameter estimation

ASJC Scopus subject areas

  • Computer Science Applications

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