This is actually all very complicated, because if you're doing e.g. velocity estimation via smoothed differences, missing a point means you've (effectively) inserted a spike-like pseudo-datapoint into your Kalman filter (or whatever) --- it will not totally crap, but will be incorrect until the 'bad' datapoint flows completely through the filter.
... Indeed. Therefore we add sensor diversity to balance the failure modes and increase availability. Having no sample means that you increase your confidence interval at warp speed to account for all possible scenarios. Very quickly your estimate becomes : « great, I am traveling at 35kph +/- 100kph. Wait. What? »
