Efficient computation of the Bayesian Cramer-Rao bound on estimating parameters of Markov models

Joseph Tabrikian, Jeffrey L. Krolik

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


This paper presents a novel method for calculating the Hybrid Cramer-Rao lower bound (HCRLB) when the statistical model for the data has a Markovian nature. The method applies to both the non-linear/non-Gaussian as well as linear/Gaussian model. The approach solves the required expectation over unknown random parameters by several one-dimensional integrals computed recursively, thus simplifying a computationally-intensive multi- dimensional integration. The method is applied to the problem of refractivity estimation using radar clutter from the sea surface, where the backscatter cross section is assumed to be a Markov process in range. The HCRLB is evaluated and compared to the performance of the corresponding maximum a-posteriori estimator. Simulation results indicate that the HCRLB provides a tight lower bound in this application.

Original languageEnglish
Pages (from-to)1761-1764
Number of pages4
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 1 Jan 1999
Externally publishedYes
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: 15 Mar 199919 Mar 1999

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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