An improved mean-square weight deviation-proportionate gain algorithm based on error autocorrelation

摘要

This paper presents an alternative approach to the gain distribution policy used in the z2-proportionate algorithm. The gain policy of the z~2-proportionate uses a rule that combines the mean-square weight deviation-proportionate gain and a uniform one to obtain the whole algorithm gain distribution, leading to very good convergence characteristics. However, such a gain combination law is dependent on the knowledge of the measurement noise variance in the system, which in practice is not always readily available. Here, aiming to circumvent such dependence, a new strategy of gain distribution based on error autocorrelation is introduced. The proposed approach makes the use of the mean-square weight deviation-proportionate gain more attractive for real-world applications. Simulation results show that the proposed algorithm outperforms the z~2-proportionate in terms of convergence characteristics for cases in which the measurement noise variance is either unknown or poorly estimated.