Kalman filter with missing measurement inputs I am a newby to Kalmar filters, but after some study, I think I understand how it works now.
For my application, I need a Kalmar filter that combines the measurement input from two sources. In the standard Kalmar filter, that is no problem at all, but it assumes that the measurement inputs from the two sensors are available at the same times. In my application, there is one new measurement from sensor 'b' for every 13 measurements of sensor 'a'.That is, 12 out of 13 times, the measurement of sensor 'b' is missing.
How would you handle that normally? Do you simply use the predicted measurements values as substitute for the missing ones? Does that not lead to overconfidence in the missing measurements? How else can it be handled?
 A: Here might be a better approach (from link) 

For a missing measurement, just use the last state estimate as a
  measurement but set the covariance matrix of the measurement to
  essentially infinity.  (If the system uses inverse covariance just set
  the values to zero.)  This would cause a Kalman filter to essentially
  ignore the new measurement since the ratio of the variance of the
  prediction to the measurement is zero.  The result will be a new
  prediction that maintains velocity/acceleration but whose variance
  will grow according to the process noise.

A: This is not a problem at all with a Kalman filter (KF). In a KF, you have a prediction step and an update step. At each time step $k$, you must predict your states at the prediction step. This is performed using a process model. If you do not have a measurement, you skip the update step. If you have a measurement, you perform the update step after the prediction step.
Edit: Keep in mind in many cases, the updates run at a lower frequency than the predictions (e.g. GPS/INS sensor fusion). Your problem sounds suitable for this framework.
A: You are absolutely right. If at a time t the measurement is missing, only the time-update is computed and the measurement update must be skipped. This is the way you shold handle the problem.
A: Don't use predicted values.   Just Bayes-fuse the likelihoods from each available observation into your posterior as they arrive, it doesn't matter how many there are at each step.
