Fast sequential least squares design of FIR filters with linear phase

The authors propose a fast adaptive least-squares algorithm for linear-phase finite-impulse-response (FIR) filters. The algorithm requires 10m multiplications per data point where m is the filter order. This reduced computation stems from the fast adaptive forward-backward least-squares method. Both linear-phase cases i.e. with constant phase delay and with constant group delay, are examined. Simulation results demonstrate that the proposed algorithm is superior to the least-mean-squares gradient algorithm and the averaging scheme used for the modified fast Kalman algorithm