Spatial-Temporal Minimum Error Random Interaction Networks for Distributed Estimation
Spatial-Temporal Minimum Error Random Interaction Networks for Distributed Estimation
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DOI：
10.1109/lsp.2022.3219354
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发表时间：
2022
影响因子：
3.9
通讯作者：
Chen Zhu;L. Jia;Zi-Jiang Yang;R. Tao
中科院分区：
文献类型：
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作者：
Chen Zhu;L. Jia;Zi-Jiang Yang;R. Tao
本文研究了分布式无线传感器网络中的自适应参数估计问题，其中考虑了通信链路中的非高斯脉冲噪声。在这种情况下，如果仍然采用传统的分布式协作策略，网络的估计性能将显著下降。针对这一问题，提出了一种新的脉冲链路噪声下的时空最小误差随机交互分布式估计策略。同时，采用最大相关熵准则和随机梯度下降法对组合因子进行更新，使网络对链路噪声具有动态、实时的自适应性。将该算法与经典的DLMS算法和一种针对脉冲链路噪声设计的最新算法进行了比较。仿真结果表明，该算法不仅能有效降低网络流量，而且在保持非合作算法优势的同时，对非高斯链路脉冲噪声具有较好的鲁棒性，并能获得最优估计性能。
In this letter, we study the problem of adaptive parameter estimation for distributed wireless sensor networks (WSNs), where the non-Gaussian impulsive noises in the communication links are considered. In such cases, if the traditional distributed collaborative strategy is still adopted, the estimation performance of the network will decline significantly. Aiming at this problem, we propose a new spatial-temporal minimum error random interaction strategy for the distributed estimation in the presence of impulsive link noises. Furthermore, the maximum correlation entropy criterion and the stochastic gradient descent method are used to update the combination factor so that the network has dynamic and real-time adaptability to the link noises. The proposed algorithm is also compared with the classic DLMS algorithm and a state-of-the-art algorithm designed for the impulsive link noises. Simulation results show that the proposed algorithm can not only reduce the network traffic effectively, but also be robust to the non-Gaussian link impulsive noises while maintaining its advantage over the non-cooperative algorithm and achieving the optimal estimation performance.