For me two things make smoothing more useful in movement than in targeting:
- Movement needs to estimate probability at arbitrary points, instead of a single peak, so the location the probability is required at isn't related to where data is available.
- Movement has much less data than targeting, so smoothing is needed to fill in gaps in knowledge.
Theoretically smoothing might help in targeting, but all my testing has shown that a simple square kernel works just as well or better, while running many times faster.
I've also considered something like Kalman filters, but they are Unimodal which doesn't work for targeting or movement at all. Perhaps particle filters, although the histogram filters we have right now in VCS also work pretty well.
re: why in movement, agreed 100%.
With WaveSim, I've tested different kernel densities (effectively smoothing formulas) in my main gun over a huge data set. There were differences, but IIRC on the order of thousandths of a percent in hit percentage (eg 12.004% vs 12.002%). Not sure of the margin of error, either... 5k battles * ~25k ticks = millions of records, and both algorithms were running on the same data set.