Assume that we want to track an object moving in 3-D space with constant velocity. Our instruments observe bearing, range and high(cylindrical coordinates). However, of an interest are rectangular coordinates. Since transformation is non-linear this requires use of extended Kalman filter.
Because transformation is non-linear between X,Y and Range,Bearing and linear between Z and high(Z is height), this problems serves as a good comparason of how well extended Kalman filter can perform. By comparing its linear estimation error in Z to non-linear estimations in X and Y, we can judge how non-familiarities effect estimation.
Your work is very helpful
I give the X(:,1)=MAT(1,:)' as actual initial condition, where MAT is the matrix of [501x6] and i'm confusing about initial observation `Z` and assumed initial condition `Xh`
The value of Z is unused from argument in proccesANDobserve and Jacobian function.
I know this is the observation vector, I edited a bit of your code for my purpose, but it crosses the actual trajectory and calculating in its opposite way. I have a matrix `MAT` of [501x6] having 1:3 for position and 4:6 for velocities, How can I set the initial observation vector and also what other initial assumptions would be set?
'Z' Stands for the observation vector and it is used in number of places for example when you compute quantity called innovation.
Hello, I didn't understand the Use of `Z` as this is unused in your code. Its always calculating but didn't use the initial array.
ok,thank you very much
Yes, here is the document this is based on
Do you have the article/journal paper that you are referring to in order to write these Matlab codes?
In order to convert to 2-D you just have to change the appropriate dimensions of matrices. You can also use the code as is and ignore one of the outputs.
please how apply this code for 2-D ?
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