Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

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Abstract

We consider the problem of estimating the parameters of autoregressive (AR) processes in the presence of white observation noise with unknown variance, which appears in many signal processing applications such as spectral estimation, and speech processing. A new non-iterative subspace-based method named extended subspace (ESS) method is developed. The basic idea of the ESS is to estimate the variance of the observation noise via solving a generalized eigenvalue problem, and then estimate the AR parameters using the estimated variance. The major advantages of the ESS method include excellent reliability and robustness against high-level noise, and also estimating the AR parameters in a non-iterative manner. Simulation results help to evaluate the performance of the ESS method, and demonstrate its robustness.

Original languageEnglish
Title of host publicationAsilomar Conference on Signals, Systems, and Computers proceedings
EditorsMichael B. Matthews
Pages1299-1303
Number of pages5
ISBN (Electronic)9781728143002
DOIs
Publication statusPublished - Nov 2019
MoE publication typeA4 Article in a conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Publication series

NameAsilomar Conference on Signals, Systems, and Computers proceedings
ISSN (Electronic)1058-6393

Conference

ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleACSSC
CountryUnited States
CityPacific Grove
Period03/11/201906/11/2019

Keywords

  • Autoregressive signals
  • Noisy observations
  • Subspace-based method
  • Yule-Walker equations

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