Let's say I would like to use an EKF to track the position of a moving robot. The EKF would not only estimate the position itself but also variables affecting the position estimate, for example IMU biases, wheel radius, wheel slip and so on.
My question is, is it better to use one big EKF (state vector containing all estimated variables) or multiple smaller EKFs (each one responsible for tracking a subset of all variables to be estimated)? Or is there no difference?
As for the example above, the EKF could be split into one for tracking position, one for estimating wheel radius and slip and one for estimating IMU biases. The position EKF would of course use the estimations output from the other concurrent EKFs and vice versa.
To me it seems it would be easier to tune and test multiple smaller EKFs rather than just one big. Are there any other advantages/disadvantages (execution time, ease of debugging etc.) assuming the resulting estimates are equal in the two approaches (or close enough at least)?