Outlier-Detection-Based Robust Information Fusion for Networked Systems

Hongwei Wang, Hongbin Li, Wei Zhang, Junyi Zuo, Heping Wang, Jun Fang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)22291-22301
Number of pages11
JournalIEEE Sensors Journal
Volume22
Issue number22
DOIs
StatePublished - 15 Nov 2022

Keywords

  • Centralized and decentralized information fusion
  • consensus
  • measurement outliers
  • networked systems (NSs)
  • nonlinear information filter (IF)
  • outlier detection
  • variational Bayesian (VB) inference

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