The hybrid cramér-rao bound and the generalized Gaussian linear estimation problem

Y. Noam*, H. Messer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper explores the Hybrid Cramér-Rao Lower-bound (HCRLB) for a Gaussian generalized linear estimation problem in which some of the unknown parameters are deterministic while the other are random. In general, the HCRLB on the non-Bayesian parameters is not asymptotically tight. However, we show that for the generalized Gaussian linear estimation problem, the HCRLB of the deterministic parameters coincides with the CRLB, so it is an asymptotically tight bound. In addition, we show that the ML/MAP estimator [1] is asymptotically efficient for the non-Bayesian parameters while providing optimal estimate of the Bayesian parameters. The results are demonstrated on a signal processing example. It is shown the Hybrid estimation can increase spectral resolution if some prior knowledge is available only on a subset of the parameters.

Original languageEnglish
Title of host publicationSAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop
Pages395-399
Number of pages5
DOIs
StatePublished - 2008
EventSAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop - Darmstadt, Germany
Duration: 21 Jul 200823 Jul 2008

Publication series

NameSAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

Conference

ConferenceSAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Processing Workshop
Country/TerritoryGermany
CityDarmstadt
Period21/07/0823/07/08

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