Abstract Combining affine projection (AP) with the generalized maximum correntropy (GMC) criterion, we propose a new family of AP-type filtering algorithms, called as APGMC, for system identification under impulsive-noise environments. By optimizing GMC of the a posterior error vector with a l2-norm constraint on the filter weight vector, APGMC avoids the computation of the inversion of the input data matrix. Simulation results validate that APGMC achieves better filtering accuracy and faster convergence rate, compared to state-of-the-art algorithms.

中文翻译：

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具有仿射投影的广义最大相关熵算法在脉冲噪声环境下进行鲁棒过滤

摘要 将仿射投影 (AP) 与广义最大相关熵 (GMC) 准则相结合，我们提出了一种新的 AP 型滤波算法系列，称为 APGMC，用于脉冲噪声环境下的系统识别。通过在滤波器权重向量上使用 l2 范数约束优化后验误差向量的 GMC，APGMC 避免了输入数据矩阵求逆的计算。仿真结果证实，与最先进的算法相比，APGMC 实现了更好的过滤精度和更快的收敛速度。