Fast moving average recursive least mean square fit

Abstract

A new approach is developed to reduce the computational complexity of a moving average Least Mean Square Fit (LMSF) procedure. For a long data window, a traditional batch approach would result in a large number of multiplication and add operations (i.e., an order N, where N is the window length). This study shows that the moving average batch LMSF procedure could be made equivalent to a recursive process with identical filter memory length but at an order of reduction in computational load The increase in speed due to reduced computation make the moving average LMSF procedure competitive for many real time processing application. Finally, this paper also address the numerical accuracy and stability of the algorithm.