Hybrid lower bound via compression of the sampled CLR function

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

11 Scopus citations

Abstract

In this paper, a new class of hybrid lower bounds on the mean-square-error of estimators is proposed. Derivation of the proposed class is performed by applying an integral transform on the centered likelihood-ratio (CLR) function. It is shown that the hybrid Cramér-Rao and Barankin bounds are the limits of convergent sequences of bounds, which are obtained from the proposed class using specific sequences of integral transform kernels. A new hybrid bound is derived from the proposed class using a sequence of integral transform kernels, which comprise a compression matrix of the sampled CLR function. In comparison with existing bounds, the proposed bound is computationally manageable and provides better prediction of the threshold region of the joint maximum-a posteriori probability maximum-likelihood estimator, in a single tone estimation scenario.

Original languageEnglish
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages602-605
Number of pages4
DOIs
StatePublished - 25 Dec 2009
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: 31 Aug 20093 Sep 2009

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Country/TerritoryUnited Kingdom
CityCardiff
Period31/08/093/09/09

Keywords

  • Hybrid bounds
  • JMAPMLE
  • Mean-square-error bounds
  • Parameter estimation
  • Performance bounds
  • Threshold SNR

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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