How Is Gain of Hybrid Sparse Adaptive Filtering Algorithm Affected by Input Correlation?

摘要

We propose a simple adaptive controlling technique for the weighting parameters that govern the metric employed in the hybrid sparse adaptive filtering algorithm. The proposed technique shifts the weights {linearly in time} from IPNLMS to TD-NLMS once an estimate of the mean squared output-error falls below some threshold. For a fair comparison between two adaptive algorithms, a gain measure based on the geometric mean of the MSE ratios is introduced. Numerical examples support the following: (i) the gain of IPNLMS against TD-NLMS drops from positive to negative as the correlation of inputs becomes strong, (ii) the gain of the proposed algorithm against TD-NLMS is approximately constant regardless of the strength of the correlation, (iii) the gain of the proposed algorithm against IPNLMS grows as an increase of the correlation.