Extended Kalman Filter Explained with Python Code

In the following code, I have implemented an Extended Kalman Filter for modeling the movement of a car with constant turn rate and velocity. The code is mainly based on this work (I did some bug fixing and some adaptation such that the code runs similar to the Kalman filter that I have earlier implemented).

Extended Kalman filter

Trajectory of the car, click on the image for large scale

References: [1] [2] [3] [4] [5]

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1 year ago

Hi, first of all thank you for you amazing video series that is helping me so much understanding the Kalman filter !

In this video, what is the C function and Ck matrix at the end, in the Update State equations ?

Jonathon Walker
7 months ago

Any way to get the /home/behnam/Kalman/2014-03-26-000-Data.csv file?

4 months ago

Hi, I like your explanation, in the video.
Actually I try to practice EKF by simulating a simple pendulum and using python code.
however I got a problem, How can I have further discussion about it. ?
If you don’t mind, would you send me your email, so I can share my short python code about my problem.

2 months ago

Amazing work.
I just have one doubt, in the given dataset, latitude and logitude values are in range of 111 and 13 respectively. Then how come output is in range from 0-100? Please explain this? I just need co-ordinates of ekf plot in terms of lat and long,so i can see how much difference when compared to gps values. If you know piece of code to get these co-ordinates, please share.

Last edited 2 months ago by Vijay