Although numerous and very good textbooks on Kalman filtering exist, some only scratch the surface, do not cover the full scope, simplify, omit important aspects of implementation, or do not bridge the gap to practice. Specifically, the following aspects are rarely addressed:

  • How to derive a state space model for my application?
  • How to parameterize the Kalman filter, especially the system and measurement noise, \( \mathbf{Q} \) and \( \mathbf{R} \)?
  • Which possibilities and strategies arise when colored noise is present or the Gaussian assumption is not fulfilled?
  • How to handle nonlinearities?

Because of this, the full potential of the fascinating Kalman filter method is not fully exploited. This page seeks to be a supplementary resource for the professional user. In particular, a collection of practical examples and knowledge beyond textbooks for students will be published here.

If you have any suggestions or questions, please do not hesitate to contact the author.