Kalman Filtering

Found this really nifty site by googling "Kalman Filter for Dummies" (NOTE: The site linked above is missing the usage of Q in the example). Nice :P Been trying to figure out Kalman filtering for the fun of it; most of the stuff I'm doing and planning to do do not involve sensor fusion but you know, you feel left out when you're only playing with one sensor, and the big guys are playing with self guided robots equipped with like a dozen sensors :P

Anyways, I usually just vomit blood and concuss after attempting to read papers and websites on kalman filters; linear systems are just not my thing, but the site linked above has an extremely good example done on just one input - exactly what I need. Following along with the calculated examples shows how straight forward it is, only requiring the tuning of one variable, R.  with R and Q to tune.

Don't think I have time this weekend, but I'd love to do a prototype in python or C, then the next stage would be to expand it to further tests with my beautiful ADXL335 with live graphing to processing - given that the Kalman filter only requires data from the previous state, it should give a much better result than what I've been using, e.g. averaging filters.