Abstract
In this paper, the problem of multi-sensor centralized state fusion with heavy-tailed process and measurement noises is considered. In order to improve the sensor fusion estimation, a novel identify-fusion strategy is proposed. An indicator, which is modelled by a Bernoulli prior, for each measurement in each sensor to identify whether the measurement is an outlier. A Student-t based hierarchical Gaussian state space model is then constructed, and the fusion is formulated as a state space estimation problem. The state and unknown parameters in the Student-t based hierarchical Gaussian state space model are jointly inferred by the variational Bayesian technique. Computer simulations are provided to demonstrate the effectiveness of the proposed method.
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