Course contents *
This course gives thorough knowledge of linear estimation theory. The main theme of the course is optimal linear estimation, Kalman and Weiner filtering, which are systematic methods to solve estimation problems with applications in several technical disciplines, for example in telecommunications, automatic control and signal processing but also in other disciplines, such as econometrics and statistics.The course also provides an introduction to optimal filtering for non-linear systems. The course assumes familiarity with basic concepts from matrix theory, stochastic processes, and linear systems theory. The course is directed towards the students who intend to work with development and research within these fields.
The following topics are covered; Basic estimation theory, time discrete and time continuous Wiener filters, time discrete Kalman filters, properties of Wiener and Kalman filters, smoothing, Extended Kalman filters, sigma-point filters and particle filters.
Intended learning outcomes *
After successfully completing the course, the student should be able to
• Understand to which type of estimation problems linear estimation can be applied.
• Understand the relationship between computational complexity, filter structure, and performance.
• Understand the relationship between optimal filtering, linear estimation, and Wiener/Kalman filtering.
• Approach estimation problems in a systematic way.
• Compute, analyze, and modify state space models.
• Derive and manipulate the time discrete and time continuous Wiener filter equations and compute the Wiener filter for a given estimation problem.
• Derive and manipulate the time discrete Kalman filter equations and compute the Kalman filter for a given estimation problem.
• Analyze properties of optimal filters.
• Implement Wiener and Kalman filters (time discrete) and state space models using Matlab.
• Simulate state space models and optimal filters, analyze the results, optimize the filter performance, and provide a written report on the findings.
• Know about common methods for optimal filtering in the case of non-Gaussian noise or non-linear models, such as Extended Kalman filter, sigma point filtering and particle filtering.
• Formulate logical arguments, orally and in writing, in a way that is considered valid in scientific publications and presentations within the topic area.