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Introduction to Minimum Mean Squared Error Filtering

February 2, 2019 by 3200 Creative

Introduction to Minimum Mean Squared Error Filtering

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  1. Question 1 of 5
    1. Question

    The generic or standard form for the minimum mean-squared error filtering problem chooses the filter weights to

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  2. Question 2 of 5
    2. Question

    The mean-squared error is defined in practical scenarios using the expected value or mean of the error signal.

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  3. Question 3 of 5
    3. Question

    Let the error signal be the difference between the reference signal and the filter output. Which of the following signals has reduced interference or noise in a noise suppression application?

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  4. Question 4 of 5
    4. Question

    Let the error signal be the difference between the reference signal and the filter output. Which of the following signals has reduced distortion due to an unknown system in an equalization application?

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  5. Question 5 of 5
    5. Question

    Which of the following characterizes the unknown system in a system modeling, estimation, or identification problem?

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Course Lessons

  • Introduction to Minimum Mean-Squared Error Filtering

  • Solving for the Minimum Mean-Squared Error Weights

  • Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm

  • Convergence, Tracking, and the LMS Algorithm Step Size

Courses

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  • 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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