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Kalman Filter for Laser Rangefinder

Question asked by jimctr on Jan 22, 2010
Latest reply on Feb 18, 2010 by gpan

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.

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