Cramér-Rao-type bound for state estimation in linear discrete-time system with unknown system parameters

Shahar Bar, Joseph Tabrikian

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

Abstract

Tracking problems are usually investigated using the Bayesian approach. Many practical tracking problems involve some unknown deterministic nuisance parameters such as the system parameters or noise statistical parameters. This paper addresses the problem of state estimation in linear discrete-time dynamic systems in the presence of unknown deterministic system parameters. A Cramér-Rao-type bound on the mean-sqaure-error (MSE) of the state estimation is introduced. The bound is based on the concept of risk-unbiasedness and can be computed recursively. It allows evaluating the optimality of the estimation procedure. Some sequential estimators for this problem are proposed such that the estimation procedure can be considered an on-line technique. Simulation results show that the proposed bound is asymptotically achieved by the considered estimators.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3477-3481
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - 4 Aug 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period19/04/1424/04/14

Keywords

  • Cramér-Rao bound
  • Kalman filter
  • MSE
  • risk-unbiased bound
  • sequential estimation

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