New conditional posterior Cramér-Rao lower bounds for nonlinear sequential Bayesian estimation

Yujiao Zheng, Onur Ozdemir, Ruixin Niu, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


The recursive procedure to compute the posterior Cramér-Rao lower bound (PCRLB) for sequential Bayesian estimators, derived by Tichavsky et al., provides an off-line performance bound for a general nonlinear filtering problem. Since the corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random variables, this PCRLB is not well suited for online adaptive resource management for dynamic systems. For online estimation performance evaluation in a nonlinear system, the concept of conditional PCRLB was proposed by Zuo et al. in 2011. In this paper, two other online conditional PCRLBs are proposed which are alternatives to the one proposed by Zuo et al. Numerical examples are provided to show that the three online bounds, namely the conditional PCRLB proposed by Zuo et al. and the two conditional PCRLBs proposed in this paper, are very close to one another.

Original languageEnglish (US)
Article number6224195
Pages (from-to)5549-5556
Number of pages8
JournalIEEE Transactions on Signal Processing
Issue number10
StatePublished - 2012


  • Nonlinear filtering
  • Particle filters
  • Posterior Cramér-Rao lower bounds

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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