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The Hybrid Cramer-Rao Lower Bound - From Practice to Theory

Hagit Messer

Open publisher page 50 citations

Abstract

In 1987, Rockah and Schultheiss (1987) 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 was 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

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What this paper is about

In 1987, Rockah and Schultheiss (1987) 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 was 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

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Available abstract

In 1987, Rockah and Schultheiss (1987) 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 was 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

Key concepts: Cramér–Rao bound, Upper and lower bounds, Estimation theory, Bayesian probability, Mathematics, Algorithm, Computer science, Random variable

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