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Understanding the Extended Kalman Filter

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Ultimâ

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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
 

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