kalman

Arthur:Carcano -- Derive yourself a Kalman filter
Introduction

The classical example of the use of a Kalman filter is the following. Say you want to program a remote piloting interface for a small robot. This robot is moving around and we want to track its position. To track the position of this robot we have two possible sources of information:

We have access to some continuous measurement of the position of the robot (say GPS)
We also know the starting position of the robot and the movements that should have been done so far ("We have commanded the wheels to move x centimeters in such or such direction."). From this two things, we can compute the position where the robot should currently stand.
Now this two sources of information may disagree, and we are left with the question of how to merge them into one. One may wish to simply average all the estimators of the position we have access to, but a more rigorous analysis is possible.
dsp  Kalman  filter  math
12 days ago by euler
GitHub - rlabbe/Kalman-and-Bayesian-Filters-in-Python: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters,
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. - rlabbe/Kalman-and-Bayesian-Filters-in-Python
bayesian  kalman  python
14 days ago by dbuscher
rlabbe/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

See online version at: https://nbviewer.jupyter.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb
Python  filters  reference  Kalman  Bayesian