论文标题
在冲动噪声下进行归一化LMP算法用于信号估计的算法
Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise
论文作者
论文摘要
在本文中，我们引入了用于图形信号处理（GSP）（GSP）的自适应图归一化的平均PTH功率（GNLMP）算法，该算法利用GSP技术（包括频带限制的过滤和节点采样）来估算脉冲噪声下的采样图形信号。不同于最小二乘算法（例如自适应GSP最小平方正方形（GLM）算法和归一化GLMS（GNLMS）算法，GNLMP算法都具有重建一个由非高斯噪声损坏具有重尾噪声的图形信号的能力。与最近引入的自适应GSP最小平均PTH功率（GLMP）算法相比，GNLMP算法减少了迭代的数量，以收敛到稳定的图形信号。也得出了GNLMP算法的收敛条件，并且GNLMP算法处理具有多个特征的多维时变图信号的能力。模拟显示GNLMP算法在估计稳态和时变图信号方面的性能要快于GLMP，而与GLMS和GNLM相比，GNLMP算法更快。
In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from least-squares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated as well. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.
