Regularization Parameter Design of Sparseness-Controlled Proportionate Affine Projection Algorithm
In various noise environments, the regularization parameter of the sparseness-controlled improved proportionate affine projection algo-rithms is difficult to be determined. To address the problem, proposes a variable regularization adaptive filtering algorithm by setting the component of the posterior error energy vector to equal that of the noise variance, termed VR- SC-IPAPA. The computer simulation re-sults verify that the proposed algorithm with a negligible additional computational cost can solve the problem of trade-off between conver-gence rate and steady-state misalignment and improve the performance of the SC-IPAPA.
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NSTL
万方数据
维普
