Outlier-Detection Based Robust Information Fusion for Networked Systems

TL;DR

This paper introduces a hierarchical outlier detection model for networked system state estimation, employing Bayesian inference and consensus strategies to improve robustness against measurement outliers.

Contribution

It develops novel centralized and decentralized robust information fusion algorithms using a hierarchical outlier detection model with Bayesian inference.

Findings

Effective outlier detection in sensor measurements.

Improved robustness over recent solutions.

Reduced computational complexity and communication overhead.

Abstract

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.