A Recursive Exponential Filter For Time-Sensitive Data
نویسنده
چکیده
A recursive formulation of an exponential smoothing filter is developed, within the framework of a least square error approach with data uncertainties that increase exponentially with time. An efficient implementation into Java is presented. By analogy to the Kalman filter, an interpretation of the gain as a ratio of uncertainties leads to a measure of validity for the recursive exponential filter. The time sensitive recursive exponential filter is then used in a detection/classification application in a natural environment with non-stationary process statistics (the concentration and size distribution of atmospheric aerosols). The performance is found to be superior to a nonadaptive Kalman filter and to a moving average filter.
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