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Introduction to Signal Estimation and Detection Theory

February 22, 2019 by 3200 Creative

This series of six lessons introduces you to the principles of signal estimation and signal detection or hypothesis testing. You will the maximum likelihood criterion for estimation and how to classify different types of hypothesis tests and the metrics used to characterize the performance of detectors such as the probability of correct detection and the receiver operating characteristic or ROC. You will learn about the likelihood ratio, which is the optimal test of simple binary hypotheses. There are no known optimal tests for more general testing scenarios, so you will learn about the generalized likelihood ratio as a principled approach for obtaining a good test.

Course Content

Lessons Status
1

Introduction to Estimation Theory

2

Parameter Estimation Criteria

3

Maximum Likelihood Estimation Examples

4

Introduction to Detection Theory

5

The Likelihood Ratio Test

6

The Generalized Likelihood Ratio Test

Filed Under: Uncategorized

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Course Lessons

  • Introduction to Estimation Theory

  • Parameter Estimation Criteria

  • Maximum Likelihood Estimation Examples

  • Introduction to Detection Theory

  • The Likelihood Ratio Test

  • The Generalized Likelihood Ratio Test

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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