Abstract
A normalized subband adaptive filter algorithm uses a fixed step size, which is chosen as a trade-off between the steady-state error and the convergence rate. In this letter, a variable step size for normalized subband adaptive filters is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, the proposed algorithm is capable of tracking in non-stationary environments. The simulation results show good tracking ability and low misalignment of the proposed algorithm in system identification.
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