Statistical and Adaptive Signal Processing

If you want to participate in this course, please register via C@mpus.

Important: The course material can be found on ILIAS

Statistical and adaptive signal processing (SASP) module moved from SS to WS. So no SASP course in SS2019.
For more information, see here.


Note: This master's lecture can also be counted as elective / elective subject of students of the old diploma program.

Contents of the Lecture

1. Introduction

2. Classical Parameter Estimation

  • Estimate and Estimator
  • Accuracy of an Estimator
  • Cramer-Rao Bound (CRB)
  • Maximum Likelihood (ML) Estimate
  • Transform of Parameters
  • Method of Least Squares

3. Bayesian Parameter Estimation

  • Basic Idea
  • Maximum A Posteriori Estimation (MAP)
  • Minimum Mean Squared Error Estimation (MMSE)
  • Linear MMSE Estimation

4. Wiener Filter

  • Introduction
  • Wiener-Hopf Equation
  • Method of Steepest Descent
  • Optimum Linear Prediction and FIR Filtering

5. Kalman Filter

  • Signal Model
  • Problem Formulation
  • Algorithm
  • Extended Kalman Filter

6. Adaptive Filter

  • Basic Principle
  • Least Mean Square Algorithm
  • General Stochastic Gradient Method
  • Recursive Least Squares Algorithm
  • Fast RLS Algorithms
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