Optimization on Tracking Performance of RLS Algorithm Using Transfer Function of Butterworth Low Pass Filter

Abstract

In order to get further optimizations on tracking performance of the RLS algorithm in nonstationarity signal processing, and to simplify the variable forgetting factor updating model, a novel variable forgetting factor updating model based on Butterworth low pass filter transfer function was proposed. The relationship between the variable forgetting factor and the tracking performance of RLS algorithm was analyzed. The updating model proposed in this paper was built based on the modification of the transfer function of Butterworth low pass filter. The model function fit the theoretical variation curve of the variable forgetting factor well. It also could be adjusted by parameters of the function order and the critical point according to the different applications. In addition, the computation of the updating model was simple and convenience. The RLS algorithm with the updating model was tested in an adaptive interference cancellation system. Some conclusions were drawn from the simulation results. The optimized RLS algorithm had a better tracking performance in nonstationarity signal processing as well as small stationary errors after convergence.