It's industry standard in aerospace, granted one usually uses the EKF with nonlinear dynamics so the covariance matrix is not estimated perfectly. That setup is also flexible enough to let you introduce of band measurements from the past but with the correct timestamp and correct.
It's kind of interesting that how particular fields chose to embrace certain particular methods; till I have worked with aerospace domain,I never had heard of the Kalman filter, whereas some of the newer and even more fundamental methods like wavelets and compressed sensing appear to have such low traction in Aerospace till now.
You probably know it by some other name. It is so classical its unlikely that one hasn't run into it in some form or the other. If you assume a hidden Markov model with Gaussian state transition and Gaussian output and work out the recursive update equation KF is what you will get. It might not have been call a Kalman filter. May be Weiner filter will ring a bell, under certain assumptions they become the same thing. If not Weiner filter, recursive least squares would surely ring a bell.
Kalman filters were invented for moon lander navigation in the Apollo program, so they had taken firm root especially in the days of limited data processing capability.
