Worst Case Cramér-Rao Bounds for Parametric Estimation of Superimposed Signals with Applications

Sze Fong Yau, Yoram Bresler

Research output: Contribution to journalArticle

Abstract

The problem of parameter estimation of superimposed signals in white Gaussian noise is considered. The effect of the correlation structure of the signals on the Cramer-Rao bounds is studied for both the single and multiple experiment cases. The best and worst conditions are found using various criteria. The results are applied to the example of parameter estimation of superimposed sinusoids, or plane-wave direction finding in white Gaussian noise, and best and worst conditions on the correlation structure and relative phase of the sinusoids are found. This provides useful information on the limits of the resolvability of sinusoid signals in time series analysis or of plane waves in array processing. The conditions are also useful for designing worst-case simulation studies of estimation algorithms, and for the design of minimax signal acquisition and estimation procedures, as demonstrated by an example.

Original languageEnglish (US)
Pages (from-to)2973-2986
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume40
Issue number12
DOIs
StatePublished - Dec 1992

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Parameter estimation
Array processing
Cramer-Rao bounds
Time series analysis
Experiments

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Worst Case Cramér-Rao Bounds for Parametric Estimation of Superimposed Signals with Applications. / Yau, Sze Fong; Bresler, Yoram.

In: IEEE Transactions on Signal Processing, Vol. 40, No. 12, 12.1992, p. 2973-2986.

Research output: Contribution to journalArticle

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