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

  • Authors:

  • J. Tabrikian;J. L. Krolik

  • Affiliations:

  • Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA;-

  • Venue:
  • ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
  • Year:
  • 1999

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Abstract

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.