I must be missing something. I passed 128 records of 1300 samples each (1300x128) through a Kalman Filter (This is basically a laser return signal - often times quite small- buried in solar noise generated by an APD - Avalanche Photo Diode) and the output appears as noisy as any one of the record inputs. Simple Averaging of these 128 records yields a signal from the noise, but I was under the impression that Kalman Filter is superior to simple averaging for lifting signal from noise. I can only conclude that I am doing something wrong. I had set my State Transition Matrix (A) and Observation Matrix (H) to an Identity Matrix, and I played around with process noise covariance(Q) and measurement noise covariance (R) values but to no avail. This is a stationary process so I employed a Simple Discrete Kalman Filter. Are there any limitations to the use of Kalman filters? Sampling Rates? Any suggestions why a filter may fail you? Thanks.