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Estimation of Power Spectra and Coherence

February 25, 2019 by 3200 Creative

This series of eight lessons addresses the signal-processing problem of estimating properties of a random signal from measurements. Five of the eight lessons concern estimation of frequency domain characteristics such as the power spectrum and coherence. You will learn about the periodogram and why averaging is necessary to obtain acceptable estimates of the power spectral density. You will learn how the averaged periodogram or Welch's method reduces the variance of the periodogram estimator at the expense of resolution loss. In the final two lessons you will learn about maximum likelihood estimation as a general tool for estimating unknown parameters in a random signal.

Course Content

Lessons Status
1

Parametric vs. Nonparametric Spectrum Estimation

2

The Periodogram

3

The Averaged Periodogram: Welch's Method

4

Power Spectrum Estimation Examples: Welch's Method

5

Estimation of Coherence and Cross Spectra

Filed Under: Uncategorized

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Course Lessons

  • Parametric vs. Nonparametric Spectrum Estimation

  • The Periodogram

  • The Averaged Periodogram: Welch’s Method

  • Power Spectrum Estimation Examples: Welch’s Method

  • Estimation of Coherence and Cross Spectra

Courses

  • Foundations

  • Time Domain LTI Systems

  • Fourier Series and Transforms

  • Sampling and Reconstruction

  • The DFT and Applications

  • The Z-Transform

  • Intro to Filter Design

  • IIR Filter Design

  • FIR Filter Design

  • Random Signal Characterization

  • Basis Representations of Signals

  • Estimation of Power Spectra and Coherence

  • Introduction to Signal Estimation and Detection Theory

  • MMSE Filtering and Least-Squares Problems

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