Sequential MCMC Estimation of Nonlinear Instantaneous Frequency

Abstract

Instantaneous frequency (IF) estimation of signals with nonlinear phase is challenging, especially for online processing. In this paper, we propose IF estimation using sequential Bayesian techniques, by combining the particle filtering method with the Markov chain Monte Carlo (MCMC) method. Using this approach, a nonlinear IF of unknown closed form is approximated as a linear combination of the IFs of non-overlapping waveforms with polynomial phase. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, the performance of this sequential MCMC approach is demonstrated and compared with an existing IF estimation technique using the Wigner distribution.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA475635

Entities

People

  • A. Papandreou-suppappola
  • D. Morrell
  • D. Simon
  • R. L. Murray
  • Yang Li

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Air Platforms
  • Biomedical

DTIC Thesaurus Topics

  • Bayes Theorem
  • Computational Complexity
  • Computational Science
  • Data Science
  • Electrical Engineering
  • Engineering
  • Filters
  • Frequency
  • Mathematical Models
  • Models
  • Monte Carlo Method
  • Particles
  • Polynomials
  • Probability
  • Sequential Monte Carlo Methods
  • Simulations
  • Waveforms

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms