Hi, as a beginner, I have some problems under the condition that there are two known variables, let's say that the objective function is f =sin(x)+cos(y) and the gradient function is obviously g = cos(x)-sin(y), how to modify the example.m, since I have no idea that how to assign the initial values, when I set the values of x and y all equal to 1, the result suggests that the dimensions is not matched. Can anybody help me to modify myfun.m and example.m to give the right answer? Thank you so much.

Hi everybody!
I really have not understood this code yet. In my case, I also study on EKF for GPS data that I want to apply EKF to due with noise and missing data in GPS data. I have one GPS data columm with more than 2000 of length. Who could show me how to do it?
Thank you so much for your kinds

Lines 51 and 53 are given by:
[x1,A]=jaccsd(fstate,x);
[z1,H]=jaccsd(hmeas,x1);
Why is x1 = fstate(x) used as the input for calculating the jacobian of the measurement equation? It makes more sense if the jacobian of the measurement equation is also evaluated at the current state x. Am I interpreting that part incorrectly?

when i highlith error between variable and its estimate (by adding a new variable err=x-xestimate) i plot err. a cycle limit (oscillation )is in this figure.and a gap appear between variable and its estimate .
is it an explanation and solution to this.

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

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