The first I heard of Kalman Filters was in articles related to the Darpa challenge ... it was my impression it was a means to process signal inputs AND combine inputs from multiple sensors (like wheel encoders plus gyro plus accelerometer) into some sort of matrix which would evaluate the most correct decision data.
From reading this thread I now understand that the Kalman Filter is a means to extract a good signal (opinion/estimate) from a noisy one - usually from ONE sensor.
Since it seems that the Kalman Filter is a very advanced topic, in First we can use simpler filtering techniques such as moving averages, throwing out "obviously bad" minimum and maximum sensor readings, and more advanced filters such as FIR and FFT. Some more discussion about filtering sensor inputs, and combining multiple sensors into decision-making software, can be found at
http://www.servomagazine.com/search.php and searching for
filter.
Searching for
sensor fusion on the same site produces some interesting articles about combining multiple sensors to make decisions, particularly the July and August 2006 articles entitled "Autonomous Robots and Multiple Sensors".