We consider state estimation for networked systems where measurements from sensor nodes are contaminated by outliers. A new hierarchical measurement model is formulated for outlier detection by integrating the outlier-free measurement model with a binary indicator variable. 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 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, an approximation distributed solution 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.
翻译:我们考虑对通过传感器节点测量的受离子污染的网络系统进行国家估计。 开发了新的等级测量模型,通过将无离子测量模型与二进制指标变量结合,进行异端检测。 先前指定为 beta- Bernoulli 的二进制指标变量,用于描述传感器的测量是名义性的还是外端的。 根据拟议的外部探测测量模型,开发了中央和分散的信息聚合过滤器。 具体地说,在集中方法中,所有测量都发送到一个聚合中心,通过使用中位场变异贝伊西亚的推断,对州和异端指标进行联合估算。 然而,在分散的方法中,每个节点仅与邻国共享信息,包括先前和可能性,以混合共识战略为基础。 之后,每个节点独立地根据自己的和共享的信息执行估算任务。 此外,提出一个近似分布式解决方案,以降低本地的计算复杂性和通信费。 模拟结果显示,拟议的算法在与最近几个稳健的解决方案相比,有效处理外端。