Discover the Kalman filter with LabVIEW and the LEGO NXT
Imagine that you are hunting Roger Rabbit. Once again you are lying in wait for that beast, because it narrowly escaped a few times and you definitely want to shoot it. You have hidden at 200m with your rifle scope screwed to your best hunting shotgun… and you are waiting. You are aware that your accuracy at that distance is 50cm, so you must be certain of your shot… and Roger is clever and overly swift. You estimate its running speed at about 1.50m per seconds, quite impressive for a rabbit, isn’t it? Your bullet is flying at 800m/sec.
You catch sight of the rabbit. It scampers in the free field. Excellent conditions; you rapidly calculate that you must target a point, 37.5cm ahead of Roger’s actual position. Bang !… and you missed it. What went wrong?
This experience is a typical daily-life problem, where you estimate that a certain event will take place at a determined location in space and at a precise moment in time, but this coincidence does not happen in reality. The main reasons for your misjudgment are on one hand least variations in the course of events and on the other hand slight errors in your appreciation. Both error sources accumulate in a complex way with the result that in reality things rarely happen as deterministically predicted.
Because your cockily neighbor always returns triumphantly from hunting and you have failed so often, you are questioning yourself, if there is a way to improve your estimation of Roger Rabbit’s position, so that you can hit that bunny. The answer to that question is yes, you can! If you are able to correct your prediction with your best observation, then you will reduce the errors to a minimum. That’s exactly what the Kalman filter is for!