Hi, thanks for the code; well written.
Can you help me out with a simple query? When we specify the number of Gaussians to (say 2), can we find the weight of each Gaussian component, (i.e weight of all samples that have label=1 and weight of all samples that have label=2)?
Hello everybody,
i have more general question about the extended kalman filter usage. what is not clear to me why EKF uses non-linear functions f and h for state prediction and estimate, while in other places the Jacobian of these functions is used.
Why the following is never used?
first calculate the liniarized state and measurements models at previous estimate point using Jacobian. Use the liniearized state transition and measurements matrix everywhere instead of non-linear in this specific iteration.
I would really appreciate your help
Thank you
Great submission, thanks!
One question though: in the parameter explanation you define inputs x and P as "a priori" state estimate and "a priori" estimated state covariance. In my understanding this is not right, as "a priori" values are only available right after the prediction step of the filter.
So, in my opinion x and P are the "a posteriori" values of the previous time step. The "a priori" values of x and P of the current time step are available after the prediction step of your filter (vals x1 and P in lines 51 and 52).
Do you agree?
Hi guys, i need some help please. I Use Matlab R2012b to try to run the code/example. I usually copy the whole code,place a new editor,highlight the example,right click,left click 'evaluate selection'(as i don't see any 'run').But on Matlab's command window, it shows the highlighted example and says "Undefined function 'ekf' for input arguments of type 'function_handle'." Please who knows what could be wrong? What could i be doing wrong? Thank you. John
hi,
the states are well estimated by EKF,but if chaging in extended state variable at middle of the simulation EKF converdge always to the initial value one.
please i want explication.
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