Applied Optimal Estimation in Engineering Systems

Knowledge

The student ...

• has advanced knowledge in understanding of linear systems in 1) various engineering applications, 2) fundamental concepts and methods on detection and estimation theory for signal processing, and 3) fundamental concepts and methods on linear systems in the presence of stochastic disturbances,
• has in-depth knowledge in the core methods of this scientific field; Kalman filter (KF) and extended Kalman filter (EFK) methodologies, and its extensions to linear dynamics for stochastic parameter estimation and prediction problems,
• can apply the knowledge in KF and EFK methodologies on developing data driven navigation systems.

Skills

The student can ...

• make critical assessment of the uncertainties in measurements and data in navigation systems,
• utilize parameter estimation to analyse multivariable system dynamics under stochastic random processes with noisy measurements
• analyse the future behavior in the state variables of dynamic systems on the basis of past incomplete noisy measurements from one or more sensors (i.e. the sensor fusion approach),
• utilize models and estimation algorithms, for both discrete-time and continuous-time dynamic systems, subject to one or more stochastic inputs and including noisy measurements from one or more sensors, including the associated algorithms in navigation systems,
• carry out a R&D project related to navigation systems under guidance.

Competence

The student can ...

• apply knowledge of KF and EFK methodologies in other fields to carry out advanced work tasks and projects,
• formulate and solve problems, such as detection of event occurrences, extracting relevant information about the event, parameter estimation, system state estimation, and sensor fusion,
• communicate relevant problems, analysis and conclusions in the field of navigation systems to experts and non-experts,
• participate in brainstorming and innovation processes related to the subject.