This letter proposes a prefiltered observation-based affine projection algorithm (APA) to achieve robustness against outliers. The conventional robust algorithm for correlated input signal, which is called the affine projection sign algorithm (APSA), was developed by using the $\mathcal {L}_1$-norm of the error signal. However, it suffers from slow tracking speed for a system change suddenly, because it can not distinguish between the time-varying system and occurrence of the impulsive noise. To overcome this problem, the proposed algorithm is induced from the matrix inequalities in no impulsive interference. The proposed algorithm determines the necessity for updating the weight vector in the noise. Hence, it has robustness and tracking ability because it discriminates between the time-varying system and occurrence of the impulsive noise. Simulations in a system identification scenario show that the proposed algorithm surpasses the APA and APSA in terms of convergence rate and tracking performance under the impulsive noise environment.

中文翻译：

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一种抗脉冲噪声的鲁棒仿射投影算法

这封信提出了一种基于预过滤观察的仿射投影算法 (APA)，以实现对异常值的鲁棒性。用于相关输入信号的传统鲁棒算法称为仿射投影符号算法 (APSA)，它是通过使用误差信号的 $\mathcal {L}_1$-norm 开发的。然而，它对系统突然变化的跟踪速度很慢，因为它不能区分时变系统和脉冲噪声的出现。为了克服这个问题，所提出的算法是在没有脉冲干扰的情况下从矩阵不等式中导出的。所提出的算法确定了更新噪声中权重向量的必要性。因此，它具有鲁棒性和跟踪能力，因为它可以区分时变系统和脉冲噪声的出现。