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

•An improved z2-proportionate algorithm based on error autocorrelation is proposed.•The novel approach does not require the knowledge of the measurement noise variance.•The new algorithm is suited for stationary and nonstationary measurement noise environment.•The new approach makes the z2-proportionate for practical applications more attractive.•Results of numerical simulation confirm the effectiveness of the proposed algorithm.

This paper presents an alternative approach to the gain distribution policy used in the z2-proportionate algorithm. The gain policy of the z2-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 z2-proportionate in terms of convergence characteristics for cases in which the measurement noise variance is either unknown or poorly estimated.