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

Hagit Messer

doi:10.1109/SAM.2006.1677208

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

Hagit Messer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Pages	304-307
Number of pages	4
DOIs	https://doi.org/10.1109/SAM.2006.1677208
State	Published - 2006
Externally published	Yes
Event	4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006 - Waltham, MA, United States Duration: 12 Jul 2006 → 14 Jul 2006

Publication series

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

Conference

Conference	4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Country/Territory	United States
City	Waltham, MA
Period	12/07/06 → 14/07/06

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10.1109/SAM.2006.1677208

Cite this

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title = "The hybrid Cramer-Rao lower bound - from practice to theory",

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.",

author = "Hagit Messer",

year = "2006",

doi = "10.1109/SAM.2006.1677208",

language = "אנגלית",

isbn = "1424403081",

series = "2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006",

pages = "304--307",

booktitle = "2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006",

note = "4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006 ; Conference date: 12-07-2006 Through 14-07-2006",

}

Messer, H 2006, The hybrid Cramer-Rao lower bound - from practice to theory. in 2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006., 1677208, 2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006, pp. 304-307, 4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006, Waltham, MA, United States, 12/07/06. https://doi.org/10.1109/SAM.2006.1677208

The hybrid Cramer-Rao lower bound - from practice to theory. / Messer, Hagit.
2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006. 2006. p. 304-307 1677208 (2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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