Fast-moving average recursive least-mean-squares fit

Abstract

A new approach is developed to reduce the computational complexity of a moving average least-mean-squares fit (LMSF) procedure. For a long data window, a traditional batch approach would result in an order N number of operations, 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 computation load. The increase in speed due to reduce computation could make the moving average LMSF procedure competitive for many real-time processing applications.