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An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise
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DOI：10.3390/s25103130.png)
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Abstract
En 中文
Event-triggered state estimation has attracted significant attention due to the advantage of efficiently utilizing communication resources in wireless sensor networks. In this paper, an adaptive robust event-triggered variational Bayesian filtering method is designed for heavy-tailed noise with inaccurate nominal covariance matrices. The one-step state prediction probability density function and the measurement likelihood function are modeled as Student's t-distributions. By choosing inverse Wishart priors, the system state, the prediction error covariance, and the measurement noise covariance are jointly estimated based on the variational Bayesian inference and the fixed-point iteration. In the proposed filtering algorithm, the system states and the unknown covariances are adaptively updated by taking advantage of the event-triggered probabilistic information and the transmitted measurement data in the cases of non-transmission and transmission, respectively. The tracking simulations show that the proposed filtering method achieves good and robust estimation performance with low communication overhead.
Keywords:
event-triggered scheduling scheme
non-Gaussian noise
robust state estimation
variational Bayesian approach
Journal
IF:
3.5
Papers:
7.1W
Citations:
20.9W