Efficient computation of the bayesian cramerrao bound on estimating parameters of markov models

Joseph Tabrikian, Jeffrey L. Krolik

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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
Title of host publicationBayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
PublisherWiley-IEEE Press
Number of pages4
ISBN (Electronic)9780470544198
ISBN (Print)0470120959, 9780470120958
StatePublished - 1 Jan 2007
Externally publishedYes


  • Bayesian methods
  • Clutter
  • Covariance matrix
  • Markov processes
  • Refractive index
  • Sea surface
  • Silicon carbide

ASJC Scopus subject areas

  • General Computer Science


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