Understanding the Extended Kalman Filter

[Ultimâ]

Hi there,

I've grasped the idea of an ordinary Kalman Filter and created a program that tracks a noisy signal. However when it comes to modifying the program to an EKF I'm at a total loss. I've spent weeks reading up on this and searching for examples, but I'm still unable to figure how to calculate the modified equations
(especially the Jacobian terms). So I have decided to ask if anyone on these forums have any expertise in this area and could possibly help me out.

Can anyone suggest how I calculate the Jacobians A_k (sometimes described as F_k) and H_k for a simple case of tracking an x-coordinate over time? I'm assuming the noise has no gain.

Thanks,
Charles

 

[Ultimâ]

Here's a link describing the EKF : https://en.wikipedia.org/wiki/Kalman_filter#Extended_Kalman_filter

Maybe now someone might be able to help?

 

[Ultimâ]

Ok, perhaps an easier question, is H a symettric matrix?