This has to be one of the best kf tutorial I have been trying to understand what exactly was going on for a long time. I'm still a bit lost when it came to calculating kf gain but it's a good start. Thank you so much.

awesome video man. The graphical example really helped me understand the concept much better. thanks a lot

Thank you for all the effort and thought that went into making this! Much appreciated

I think this is the best vidoe that explained KF. Thank you so much, this is great help.

The recursive process you explain was pure gold as it cleared the doubt completely relating to previous and current time analysis. Keep posting such videos.

Thank you so much! Truly you are the best educators on this platform in the area of signals, control, estimation, etc... Please keep posting videos!

best kalman filter explanation I could find! tk so much :)

I really appreciate the way you explain the algorithms technically. I am kindly waiting EKF which is used for 3D tracking. Many thanks!

Thank you for making this intuitive video. :D I have learned many things from your KZfaq channel.

Iain is such a bossman! Thanks alot, your work is very appreciated

Crystal clear thanks prof.

hey, thank you so much, u are a phenomenal teacher

Thank you sir.. You are amazing as always..

Dear Iain, thanks for the simple and informative lecture. I really enjoyed it. I wonder if you have a lecture on the Extended Kalman filter. Thank you.

Very helpful thanks a lot.

Thank You. On the right upper corner, in the third equation, where comes the supscript l (wl vl)? In the Dirac Impuls there are 2 subscripts again.

Great video! I have reached a point where I understand the concept and implementation of a kalman filter, but what is unclear is how to place my data, observations and measurements into matrix form to carry out the these calculations. Do you have any suggestion for the concepts that I should study to understand this?

hello. can we estimate the unobservable component of the kalman filter by the least squares method?

Hi, can you do a video on de/companding in imagers. I understand the function but not getting exactly how this is done.

Interesting. This suggests that implementing a Kalman filter into a control scheme is extremely calculation intensive, and gets rapidly more intensive as the system order increases. Means selection of the control processor is very important.

Dr can we use this Kalman filter to track where the storm is moving next?

great video! thanks! can you please do IMM filter ??

Thanks a lot. The video is so cool. But I have a question. If the Matrix F, H, and G are unknown, can kalman filter work? I guess maybe those matrix could be estimated, but I don't know how...

Do you have any code in C, or Assembly language, that execute Kalman filter? It would be nice to have a microcontroller progect with input from sensors, to measure the noise and present state, and a propeller blades acting as motion input? The deflection blades can act based on feedback produced from the mcu. Any sample code around?

At minute 4:41, concerning delta_kl, should not instead R_k within the matrix the term R_l to be appeared? Thanks!

Hi sir I trying to apply kalman filter for range bearing model, but I don't know correct method to implement, can you please derive it for me, or share that has , because I am having 5 books none of them explains how it can be applied

Could you please do a tutorial on Kalman Bucy filger as well? Thanks a million.

Another excellent lecture. Thank you Iain, you are amazing! I have two questions. 1. Do you use prime to denote matrix transpose? in particular the H'_k in the Kalman gain is the transpose of the H_k, I assume? 2. As mentioned omega_k and v_k are gaussian noise vectors. In the first equation omega_k is multiplied by a matrix G_k , while in the second equation we simply have v_k. I wonder what is the G_k, is it a characteristic of a system (like F_k and Gamma_k)? My feeling is that there is a noise omega_k at time=k and then we multiply it by G_k to define its effect on time=k+1. While, in the second equation we do observation at time k and the v_k being the gaussian noise of that observation doesn't need to be multiplied by any matrix. Is there a good example to illustrate this G_k factor? Thanks

What measured value Zk Is Zk open loop measured value,I mean without feed back. As I understood Current output = Predicted Value + gain (open loop measured value Zk - Predicted Value) If open loop measured value Zk greater than Predicted value Then (open loop measured value Zk - Predicted Value) is positive that mean Predicted and open loop measured value are in same same direction So final current output is Predicted Value + difference between open loop measured value Zk and Predicted value. final current output is also in the direction of Zk If open loop measured value Zk equal to Predicted value Then (open loop measured value Zk - Predicted Value) is zero that mean Predicted and open loop measured value are same So final current output is Predicted Value + 0 final current output is also in the direction of Zk If open loop measured value Zk less than Predicted value Then (open loop measured value Zk - Predicted Value) is negative that mean Predicted and open loop measured value are in opposite direction So final current output is Predicted Value - difference between open loop measured value Zk and Predicted value. final current output is also in the opposite direction of Zk Once final current output is calculated then Zk(open loop measured value) becomes final calculated value hence Predicted value is equal to final current output but ship is moving always its measured value and direction value is keep changing so to filter out the error there is need of prediction based on previous experience, prediction and open loop measured value and direction to be minimum this is done by filter(high pass filter or difference fiter or derivative)to correct error. ---------- ------------------ ------------------ | PLANT | ---Zk---> | HPF | ---Xk---> | Ship | X^k-1--->| | ------------------- |^ ------------------- X^k -1 | |Xk ------------------------- | Prediction +| | Delay | --------------------------

Hello professor could you please do a video about Wiener filters asap?.. There are literally no good resources of wiener filter for usage of audios available in the internet