The hybrid Cramer-Rao lower bound - from practice to theory

Hagit Messer*

*Corresponding author for this work

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

Abstract

In 1987, Rockah and Schultheiss [1] introduced the Hybrid Cramer-Rao Lower Bound (HCRLB) as an extension of the classical Cramer-Rao bound (CRLB). Whereas the classical CRLB is applicable to the estimation of non-random parameters, and the Bayesian CRLB applies to random parameters, the HCRLB is applicable to the joint estimation of random and non-random parameters. In this paper wes review the basic theory of the multi-parameter Cramer-Rao type bounds in a unified framework. Then, we discus the limitations of the HCRLB which may explain why it has not been introduced till needed for a certain array processing application, where it has shown to be useful.

Original languageEnglish
Title of host publication2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Pages304-307
Number of pages4
DOIs
StatePublished - 2006
Event4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006 - Waltham, MA, United States
Duration: 12 Jul 200614 Jul 2006

Publication series

Name2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006

Conference

Conference4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Country/TerritoryUnited States
CityWaltham, MA
Period12/07/0614/07/06

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