If you want to participate in this course, please register via C@mpus.
Important: The course material can be found on ILIAS
***NEWS***
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