An Algorithm for Efficient Estimation of Superimposed Exponential Signals

Abstract

A computational algorithm is given for obtaining asymptotically efficient estimates of the unknown complex amplitudes and frequencies in a superimposed exponential model for signals. It is shown that the variance covariance matrix of these estimates are asymptotically the same as that for the maximum likelihood estimates and thus attain the Cramer-Rao lower bound. Keywords: Equivariation linear prediction; Forward and backward linear prediction; Maximum likelihood estimate; Superimposed exponential signals.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1989
Accession Number
ADA217218

Entities

People

  • Calyampudi Radhakrishna Rao
  • Mosuk Chow
  • Z. D. Bai

Organizations

  • Pennsylvania State University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Amplitude
  • Covariance
  • Data Science
  • Decision Theory
  • Frequency
  • Governments
  • Information Science
  • Military Research
  • Multivariate Analysis
  • New York
  • Simulations
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Statistical inference.