Windup properties of recursive parameter estimation algorithms in acoustic echo cancellation

The windup properties of a recursive parameter estimation algorithm recently suggested by Stenlund and Gustafsson (SG) are investigated in comparison to a number of well-known techniques such as the normalized least squares algorithm (NLMS) and the Kalman filter (KF). An acoustic echo cancellation application is used as a benchmark for comparing the properties of different approaches. The basic performance of the SG method, both for white and colored input signal, appears to be similar to that of the KF and superior to the NLMS. When the energy in the input signal decreases, the algorithm performs best of all considered estimation schemes. Once the solution of the Riccati equation of the algorithm converged to a user defined point, it will stay there even though the input excitation is reduced. This explains the good anti-windup properties of the method.