Conditional posterior Cramér-Rao lower bounds for nonlinear recursive filtering

Long Zuo, Ruixin Niu, Pramod K. Varshney

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

7 Scopus citations

Abstract

Posterior Cramér Rao lower bounds (PCRLBs) [1] for sequential Bayesian estimators pro-vide performance bounds for general nonlinear filtering problems and have been used widely for sensor man-agement in tracking and fusion systems. However, the unconditional PCRLB [1] is an off-line bound that is obtained by taking the expectation of the Fisher infor-mation matrix (FIM) with respect to the measurement and the state to be estimated. In this paper, we in-troduce a new concept of conditional PCRLB, which is dependent on the observation data up to the cur-rent time, and adaptive to a particular realization of the system state. Therefore, it is expected to provide a more accurate and effective performance evaluation than the conventional unconditional PCRLB. However, analytical computation of this new bound is, in gen-eral, intractable except when the system is linear and Gaussian. In this paper, we present a sequential Monte Carlo solution to compute the conditional PCRLB for nonlinear non-Gaussian sequential Bayesian estimation problems.

Original languageEnglish (US)
Title of host publication2009 12th International Conference on Information Fusion, FUSION 2009
Pages1528-1535
Number of pages8
StatePublished - Nov 18 2009
Event2009 12th International Conference on Information Fusion, FUSION 2009 - Seattle, WA, United States
Duration: Jul 6 2009Jul 9 2009

Publication series

Name2009 12th International Conference on Information Fusion, FUSION 2009

Other

Other2009 12th International Conference on Information Fusion, FUSION 2009
CountryUnited States
CitySeattle, WA
Period7/6/097/9/09

Keywords

  • Bayesian estimation
  • Kalman filters
  • Particle filters
  • Posterior Cramér Rao lower bounds

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software

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