Cyclic Bayesian Cramér-Rao bound for filtering in circular state space

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

Abstract

Mean-squared-error (MSE) lower bounds are widely used for performance analysis in stochastic filtering problems. In many problems of this type, the nature of part of the unknown state parameters is circular or periodic. In this case, we are interested in the modulo-T estimation errors and not in the plain error values. Thus, the MSE risk and conventional MSE bounds are inappropriate for periodic stochastic filtering problems. A commonly used risk for periodic parameter estimation is the mean-cyclic-error (MCE). In this paper, we derive a cyclic version of the Bayesian Cramér-Rao bound (BCRB) on the MCE of any recursive filter. The performance of the cyclic BCRB is evaluated for phase tracking and compared to the MCEs of existing filters.

Original languageAmerican English
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
Pages734-741
Number of pages8
ISBN (Electronic)9780982443866
StatePublished - 14 Sep 2015
Event18th International Conference on Information Fusion, Fusion 2015 - Washington, United States
Duration: 6 Jul 20159 Jul 2015

Publication series

Name2015 18th International Conference on Information Fusion, Fusion 2015

Conference

Conference18th International Conference on Information Fusion, Fusion 2015
Country/TerritoryUnited States
CityWashington
Period6/07/159/07/15

Keywords

  • Bayesian Cramér-Rao bound (BCRB)
  • Mean-squared-error (MSE) lower bounds
  • mean-cyclic-error (MCE)
  • periodic stochastic filtering
  • phase tracking

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing
  • Computer Networks and Communications

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