  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.

Applications