Smoothing out Noisy Data

If you have data for the whole flight available to you then a good approach is Kalman smoothing. If you want estimates during the flight you want Kalman filtering. Seems like you're interested in the former. The difference is that Kalman smoothing uses data from the entire flight to estimate values at each point in time while Kalman filtering only uses the "past" to make its estimates. Some googling found a readable looking paper on both Kalman filtering and smoothing. But you need to tune it with an estimate of the properties of the noise from your sensors. I think that in the real world engineers often guess these values. (Or maybe just the ones I know...)

have you heard of the Kalman filter: that is a very popular approach for this kind of problems (eg:satellite tracking) where you just have noisy observations, and a known dynamics (Newton's laws of motion + the different characteristics of your rocket). This is also very easily implemented - see wikipedia

Convolve the data (simplest thing is to use a box) and renormalize. Use the conv command in MATLAB.

EDIT: You might also consider splines. Keep the dicontinuity of higher derivatives in mind if you do this.