Lower bounds on the mean square estimation error

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


A general class of lower bounds on the mean square error (mse) in random parameter estimation is formulated. These bounds are generated using functions of the parameter and the data that are orthogonal to the data. Aparticular choice in the class yields a new lower bound which is superior to both the Cramer-Rao and Bobrovsky-Zakai lower bounds.

Original languageEnglish
Title of host publicationBayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
PublisherWiley-IEEE Press
Number of pages1
ISBN (Electronic)9780470544198
ISBN (Print)0470120959, 9780470120958
StatePublished - 1 Jan 2007


  • Diffusion processes
  • Estimation error
  • Filtering
  • Mean square error methods
  • Modulation
  • Oceans
  • Parameter estimation


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