Quaternion-Based Extended Kalman Filter for Fixed-Wing UAV Attitude Estimation
http://philsal.co.uk/quaternion-based-extended-kalman-filter-for-fixed-wing-uav-attitude-estimation
Quaternion-Based Extended Kalman Filter for Fixed-Wing UAV Attitude Estimation
Quaternion-Based Extended Kalman Filter for Fixed-Wing UAV Attitude Estimation
http://philsal.co.uk/quaternion-based-extended-kalman-filter-for-fixed-wing-uav-attitude-estimation
If I understand it right, If I had only the the accelerometer without the magnetometer then the C matrix ( observation) would change by removing the last 3 rows. Moreover , what if I didn't have the Va ? how the accelerometer model look like then ? should I just assume it to be zero ?
I mean I only have the accelerometer in my update step.
Thanks
and by the way .. great work !
Hi
great work
I have already done the state estimation with linear Kalman filter... Now I want to do it with your Code (EKF) and publish the comparison...
but I need more description of how you matlab function works
I added that to my simulink model as matlab function that updates each iterates
I have a few questions:
What is Va? How should I know the velocity?? I dont have the velocity value :|
I ran the simulation with :
Va , MagDec = 0
NDivT =1
but I got NaN value as output
Can you please help me with an example for implementing your code in a working project?
I will publish the comparison result in youtube and put the link to your github as source
Thanks
Hi,
First of all, very great work ! It's not an issue, it's just a question about your code.
I have a board build witn an gyro and accel, I don't have mag, is it possible to use your code without mag? What the results if I don't provide any data or zero data for mag?
Can I set mag values at 0 also Va airpseed?
Thanks
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