Abstract
In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavy-tailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.
Highlights
Distributed state estimation is important in distributed sensor networks [1], [2]
It can be seen from the figures that the root mean square error (RMSE) of the distributed variational Bayesian Student-t CI (DVBSCI) method gives the worst performance
This is because DVBSCI algorithm only models the outliers of measurement noise and does not consider the outliers of process noise
Summary
Distributed state estimation is important in distributed sensor networks [1], [2]. Due to the complex environment, the communication, perception and processing capabilities of the distributed sensor network will be limited. It is extended to nonlinear cases in [28] where hybrid consensus strategy [9], [11] is used These methods can only handle the scenarios with heavy-tailed measurement noise and well-behaved process noise. The robust Student-t filters for heavy-tailed process and measurement noises have been proposed in [19], [33]–[35] for single sensor. This method has less computational complexity, is easy to apply and can deal with high-dimensional problems. A distributed consensus Student-t filter is presented in this paper to handle both heavy-tailed process and measurement noises for distributed sensor networks.
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