Frequency estimation of a single real-valued sinusoid: An invariant function approach

Candan Ç., Çelebi U.

Signal Processing, vol.185, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 185
  • Publication Date: 2021
  • Doi Number: 10.1016/j.sigpro.2021.108098
  • Journal Name: Signal Processing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, zbMATH
  • Keywords: Maximum likelihood estimation, Gridless search, Frequency estimation, Real-valued sinusoids, Parameter estimation, PARAMETER-ESTIMATION, DFT, INTERPOLATION
  • Middle East Technical University Affiliated: Yes

Abstract

© 2021 Elsevier B.V.An invariant function approach for the computationally efficient (non-iterative and gridless) maximum likelihood (ML) estimation of unknown parameters is applied on the real-valued sinusoid frequency estimation problem. The main attraction point of the approach is its potential to yield a ML-like performance at a significantly reduced computational load with respect to conventional ML estimator that requires repeated evaluation of an objective function or numerical search routines. The numerical results indicate that the suggested estimator closely tracks the Cramer-Rao bound in the asymptotic region and performs very close to the ML estimator in other regions.