Filtering of Time Correlated Data,
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Convential filter theory neglects autocorrelation of errors in input data. The assumption that this can be done lead to erroneous asymptotic behavior of filter smoothing factors at high data rates. This has been demonstrated by introducing a more realistic correlation model into the theories of the moving arc least squares filter and a simple recursive filter. The smoothing factor of the former in the revised theory is asymptotically independent of rate, whereas the conventional theory predicts 1square root of r behavior. The theory of recursive filter exhibits a finite optimum rate. The optimum rate is infinite if correlation of input data errors is neglected.