Robust Diffusion Adaptive Networks with Noisy Link and Input
Robust Diffusion Adaptive Networks with Noisy Link and Input
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DOI：
10.23919/ccc55666.2022.9902578
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Chen Zhu;Lijuan Jia;S. Kanae;Zi-Jiang Yang
中科院分区：
文献类型：
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作者：
Chen Zhu;Lijuan Jia;S. Kanae;Zi-Jiang Yang
本文研究了多智能体分布式网络的自适应参数估计问题，其中网络节点的输入回归向量含有高斯噪声，而输出值和通信链路受到脉冲噪声的污染。在这种情况下，传统的扩散LMS算法和大多数最先进的鲁棒分布式算法的输出脉冲噪声的估计性能将显着下降。针对这一问题，本文提出的最小干扰偏差补偿扩散最小均方（MDBC-DLMS）算法能够有效抑制噪声干扰，并获得可接受的目标参数向量估计结果。MDBC-DLMS采用最小扰动原则动态更新扩散算法的组合系数，有效抑制输出和链路脉冲噪声。同时对输入噪声方差信息进行动态实时估计，以补偿输入噪声引起的估计偏差。仿真结果表明，该方法具有良好的估计性能和有效性，同时能够准确估计出输入噪声的方差信息。
In this paper, we study the problem of adaptive parameter estimation for multi-agent distributed networks, where the input regression vectors of network nodes contain Gaussian noises, while the output values and the communication link are polluted by impulse noises. In this case, the estimation performance of traditional diffusion LMS algorithms and most of the state-of-the-art robust distributed algorithms for output impulse noises will degrade significantly. Aiming at this problem, the Minimal Disturbance Bias-Compensated Diffusion Least Mean Square (MDBC-DLMS) algorithm proposed in this paper can effectively suppress noise interference and achieve an acceptable estimation result of the target parameter vector. MDBC-DLMS uses the principle of minimal disturbance to dynamically update the combination coefficients of the diffusion algorithm to effectively suppress the output and link impulse noise. At the same time, it performs dynamic real-time estimation of the input noise variance information to compensate for the estimation bias caused by the input noise. The simulation results show the excellent estimation performance and effectiveness of the method proposed in this paper, and it can accurately estimate the variance information of input noise at the same time.