This is a classical tracking problem, on which you'll find tons of scientific literature with a lot of different approaches. The bad news is, if you can't find a library which does the job for you, you'll have to look into the math.
Utilizing a Kalman Filter goes into the right direction, since can it estimate a state (position, velocity) from indirect measurement data. Since your multiliterations are a non-linear mapping for the measurement data, you need a non-linear estimator.
My standard recommendation for such problems is the Unscented Kalman Filter because of its (relative) algorithmic simplicity and its high robustness. It can also take care of your multiliteration, because multiple different measurement within one time step are allowed. As for the Kalman Filter you'll also a need a motion model - a simple (linear) one might do the job, since I assume you're tracking normal planes (not highly maneuverable jet fighters). Unfortunately I'm not aware of any appropriate implementation - for instructions how to implement one efficiently, read (the math behind it is not trivial):
Merwe, R. V. D. & Wan, E. A. The square-root unscented Kalman filter for state and parameter-estimation in International Conference on Acoustics, Speech, and Signal Processing, 2001, 3461-3464
For a (low accuracy) quick and dirty solution, implement a FIR Low-path Filter for each dimension. You'll find tools on the web (e.g. here), which can generate code for you.