**kalman**

Arthur:Carcano -- Derive yourself a Kalman filter

12 days ago by euler

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
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.

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,

bayesian
kalman
python

14 days ago by dbuscher

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

14 days ago by dbuscher

rlabbe/Kalman-and-Bayesian-Filters-in-Python

5 weeks ago by Morrad

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

5 weeks ago by Morrad

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,

bayesian python algorithms kalman filter

5 weeks ago by cgrin

bayesian python algorithms kalman filter

5 weeks ago by cgrin