TY - JOUR
T1 - Outlier-Detection-Based Robust Information Fusion for Networked Systems
AU - Wang, Hongwei
AU - Li, Hongbin
AU - Zhang, Wei
AU - Zuo, Junyi
AU - Wang, Heping
AU - Fang, Jun
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2022/11/15
Y1 - 2022/11/15
N2 - 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.
AB - 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.
KW - Centralized and decentralized information fusion
KW - consensus
KW - measurement outliers
KW - networked systems (NSs)
KW - nonlinear information filter (IF)
KW - outlier detection
KW - variational Bayesian (VB) inference
UR - http://www.scopus.com/inward/record.url?scp=85140747800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140747800&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2022.3212908
DO - 10.1109/JSEN.2022.3212908
M3 - Article
AN - SCOPUS:85140747800
SN - 1530-437X
VL - 22
SP - 22291
EP - 22301
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 22
ER -