Li Xinyu, Chen Feng, Shi Qing, Cao Yue, Yan Fei, Zhou Bingpeng
School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.
College of Artificial Intelligence, Southwest University, Chongqing, 400715, China.
ISA Trans. 2023 Sep;140:237-249. doi: 10.1016/j.isatra.2023.06.012. Epub 2023 Jun 13.
The problem of robust distributed estimation over dynamic and streaming graph signals is investigated in this paper. Existing works related to distributed estimation over dynamic and streaming graph signals are mainly derived from the Mean-Square-Error criterion, and they are vulnerable to non-Gaussian noise. Therefore, a new kind of diffusion Mixture correntropy (d-MC) algorithm is developed to deal with the disadvantage in this paper. Incorporating the diffusion strategy and a novel cost function, the proposed algorithm could accurately estimate the graph filter parameter with the dynamic and streaming graph signals, and achieve desirable performance under both Gaussian and impulsive noise environment. Besides, the theoretical analysis results of mean and mean-square stability are derived. Simulations on various case studies indicate the desirable performance of proposed d-MC algorithm by comparing it to other benchmarks.
本文研究了动态和流图信号上的鲁棒分布式估计问题。现有与动态和流图信号分布式估计相关的工作主要基于均方误差准则，并且它们易受非高斯噪声影响。因此，本文开发了一种新型的扩散混合核相关熵（d-MC）算法来处理这一缺点。该算法结合了扩散策略和一种新颖的代价函数，能够利用动态和流图信号准确估计图滤波器参数，并在高斯和脉冲噪声环境下均能实现理想性能。此外，还推导了均值和均方稳定性的理论分析结果。通过与其他基准算法比较，各种案例研究的仿真结果表明了所提出的d-MC算法具有理想性能。
