Set-Membership Adaptive Reduced-Rank Affine Projection Algorithm

Set-membership filtering approach with the method of adaptive reduced-rank affine projection algorithm is presented. The distance between the present tap-weight vector and the update is used to accelerate the convergence and decreasing the update rates of proposed algorithm. For the error upper bound constraint, the adaptive averaging threshold parameter is introduced using the estimated auto-correlation between present and previous estimated error vector for controlling the update step-size. Simulation results of proposed algorithm verify the good performance concerning to the amount of updates and convergence rate compared with existing algorithm.

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