This program is based on using normal influence diagrams as described in Kenley's doctoral dissertation (Kenley, Chapter 2) to perform optimal filtering and prediction for linear multistage processes ...

Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Jupyter Notebook so that you can run and modify the code in your browser. What ...

If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...

This course introduces the Kalman filter as a method that can solve problems related to estimating the hidden internal state of a dynamic system. It develops the background theoretical topics in state ...

As a follow-on course to "Kalman Filter Boot Camp", this course derives the steps of the linear Kalman filter to give understanding regarding how to adjust the method to applications that violate the ...

If you’re looking to improve the stability of your self balancing robot you might use a simple horrifying equation like this one. It’s part of the journey [Lauszus] took when developing a sensor ...

Abstract: Real-time control and estimation are pivotal for applications such as industrial automation and future healthcare. The realization of this vision relies heavily on efficient interactions ...

Abstract: This study develops an enhanced state estimation framework by integrating the Kalman filtering mechanism with a Gamma Pearson VII (GaPV) hybrid probability model to address non-stationary ...