A novel adaptive filtering algorithm is devised and derived for group-sparse system identification. To adequately make use of the group-sparsity in satellite communication and network echo channels, we integrate a mixed-norm constraint into the proportionate normalized least mean fourth (PNLMF) algorithm, which is referred as mixed-norm constrained PNLMF (MNC-PNLMF) algorithm. The MNC-PNLMF algorithm is derived and analyzed in detail. Serval experimental experiments are constructed to validate the effectiveness of the MNC-PNLMF. The experimental results demonstrate that the MNC-PNLMF outperforms the NLMF, PNLMF, zero-attraction NLMF (ZA-NLMF), and reweighted ZA-NLMF (RZA-NLMF) for group-sparse system identification.

提出了一种新颖的自适应滤波算法，用于组稀疏系统识别。为了充分利用卫星通信和网络回波通道中的组稀疏性，我们将混合范数约束整合到比例归一化最小均等四次（PNLMF）算法中，该算法称为混合范数约束PNLMF（MNC-PNLMF）算法。MNC-PNLMF算法得到了详细的推导和分析。进行了val工实验实验，以验证MNC-PNLMF的有效性。实验结果表明，对于组稀疏系统识别，MNC-PNLMF优于NLMF，PNLMF，零吸引NLMF（ZA-NLMF）和重新加权的ZA-NLMF（RZA-NLMF）。