**dsp**

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

filter
python
Kalman
Bayesian
dsp
control

5 hours ago by vitaminCPP

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

5 hours ago by vitaminCPP

www.rossum-electro.com

2 days ago by rsewan

Rossum Electro-Music creates uniquely powerful tools for electronic music production. Driven by the creative and technological vision of electronic music pioneer Dave Rossum, Rossum Electro-Music is the culmination of Dave’s 45 years of designing industry-defining instruments and transformative technologies. Starting with his co-founding of E-mu Systems, Dave provided the technological leadership that resulted in what many consider the premier professional modular synthesizer system. Today, Dave and Rossum Electro-Music are building on that 45 year history to bring a new level of creativity, innovation, and quality to the electronic musicians, producers and sound designers of the 21st century.

synth
modular
eurorack
digital
unique
dsp
manufacturer
2 days ago by rsewan

Derive yourself a Kalman filter

9 days ago by lenciel

Arthur Carcano's online presence.

math
dsp
filter
9 days ago by lenciel

Arthur:Carcano -- Derive yourself a Kalman filter

dsp

11 days ago by geetarista

Arthur Carcano's online presence.

11 days ago by geetarista

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