Outlier-Detection-Based Robust Information Fusion for Networked Systems
Outlier-Detection-Based Robust Information Fusion for Networked Systems
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DOI：
10.1109/jsen.2022.3212908
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发表时间：
2019-09
影响因子：
4.3
通讯作者：
Hongwei Wang;Hongbin Li;Wei Zhang;J. Zuo;Heping Wang;Jun Fang
中科院分区：
文献类型：
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作者：
Hongwei Wang;Hongbin Li;Wei Zhang;J. Zuo;Heping Wang;Jun Fang
我们考虑网络系统（NSs）的状态估计，其中来自传感器节点的测量值受到异常值的污染。将无异常点测量模型与每个传感器的二元指标变量相结合，建立了一种新的分层测量模型，用于异常点检测。二元指标变量，它被分配一个β -伯努利先验，被用来表征传感器的测量是标称的还是一个离群值。基于所提出的离群点检测测量模型，分别开发了集中式和分散式信息融合滤波器。具体来说，在集中式方法中，所有的测量数据都被发送到一个融合中心，在那里，通过采用平均场变分贝叶斯（VB）推理，以迭代的方式联合估计状态和离群指标。然而，在去中心化方法中，每个节点只与基于混合共识策略的邻居共享其信息，包括先验和可能性。然后，每个节点根据自己的共享信息独立执行估计任务。此外，提出了一种近似的分布式解决方案，以降低局部计算复杂度和通信开销。仿真结果表明，与现有的几种鲁棒解相比，该算法在处理异常值方面是有效的。
We consider state estimation for networked systems (NSs), where measurements from sensor nodes are contaminated by outliers. A new hierarchical measurement model is formulated for outlier detection by integrating an outlier-free measurement model with a binary indicator variable for each sensor. The binary indicator variable, which is assigned a beta-Bernoulli prior, is utilized to characterize if the sensor’s measurement is nominal or an outlier. Based on the proposed outlier-detection measurement model, both centralized and decentralized information fusion filters are developed. Specifically, in the centralized approach, all measurements are sent to a fusion center where the state and outlier indicators are jointly estimated by employing the mean-field variational Bayesian (VB) inference in an iterative manner. In the decentralized approach, however, every node shares its information, including the prior and likelihood, only with its neighbors based on a hybrid consensus strategy. Then each node independently performs the estimation task based on its own and shared information. In addition, a distributed solution with an approximation is proposed to reduce the local computational complexity and communication overhead. Simulation results reveal that the proposed algorithms are effective in dealing with outliers compared with several recent robust solutions.