I understand how to use Kalman filter for fusing two estimates and that essentially it fuses predicted state estimate produced by system dynamics update and observable state estimate produced by taking measurement.
However what if I have more than one measurement source for any of the state variables? The only way I can think of is to maintain two measurement models each of them having it's own covariance matrix and for each prediction step calculate two measurement updates using the following sequence:
- Predict state and covariance matrix using system dynamics model.
- Obtain state posterior estimate using measurement update.
- Use state posterior obtained in step 2 for the second measurement update.
Unfortunately I have not enough confidence in math in order to check the validity of this assumption.