This work deals with the block-sparse system identification problem on the basis of the maximum correntropy criterion (MCC). The MCC is known for its robustness against non-Gaussian noise and interference in many signal processing applications. With the aim of exploiting the block-sparse property of the system, we introduce a regularization function into the standard cost function of MCC. Based on the modified cost function, an online kernel adapting strategy is developed to further improve the estimation accuracy. Steady-state performance analysis is conducted to explore the behavior of the proposed method. The simulation results illuminate the validity of the theoretical analysis and confirm the superiority of the proposed method in block-sparse system identification through comparisons with state-of-the-art MCC techniques.

这项工作基于最大熵准则（MCC）处理块稀疏系统识别问题。MCC在许多信号处理应用中对非高斯噪声和干扰的鲁棒性而闻名。为了利用系统的稀疏性，我们将正则化函数引入MCC的标准成本函数中。基于修正后的成本函数，开发了一种在线核自适应策略，以进一步提高估计精度。进行稳态性能分析以探索所提出方法的行为。仿真结果通过与最新的MCC技术进行比较，阐明了理论分析的有效性，并证实了该方法在块稀疏系统识别中的优越性。