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Maximum likelihood estimation of structural wave components from noisy data.

Peter J Halliday1, Karl Grosh

  • 1Ford Motor Company, Dearborn, Michigan 48124-4076, USA.

The Journal of the Acoustical Society of America
|May 11, 2002
PubMed
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This study introduces a new framework for signal model parameter estimation using maximum likelihood estimation. The method accurately identifies wave components in structures, even with noisy data and non-uniform sampling.

Area of Science:

  • Signal processing
  • Mathematical modeling
  • Structural analysis

Background:

  • Accurate parameter estimation is crucial for understanding signal models.
  • Existing methods are limited in the types of functional bases and sampling conditions they can handle.
  • Extending these methods is necessary for broader applications in structural analysis.

Purpose of the Study:

  • To develop a general framework for parameter estimation in signal models.
  • To extend maximum likelihood estimation to functions with separable variables.
  • To accommodate diverse functional bases and non-uniform spatial sampling.

Main Methods:

  • Application of maximum likelihood estimation theory.
  • Development of a general framework for separable functions.

Related Experiment Videos

  • Extension to include exponential functions with nonconstant amplitudes and Bessel functions.
  • Adaptation for nonuniform spatial sampling.

    • Main Results:

    • Demonstrated viability and accuracy of the technique.
    • Successful estimation of exponential and Bessel function model parameters.
    • Effective identification of wave components in one-dimensional structural elements using noisy simulation data.

    Conclusions:

    • The developed framework provides a versatile tool for signal model parameter estimation.
    • The method shows high accuracy and robustness, even with noisy data.
    • This technique offers significant potential for applications in structural health monitoring and signal analysis.