# An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise

## TL;DR

This paper introduces a new filtering method for wireless sensor networks that efficiently uses communication resources while handling heavy-tailed noise.

## Contribution

The novelty lies in an adaptive robust event-triggered variational Bayesian filtering method for heavy-tailed noise with inaccurate covariances.

## Key findings

- The proposed method models prediction and measurement functions as Student’s t-distributions for robustness.
- Joint estimation of system states and covariances is achieved using variational Bayesian inference and fixed-point iteration.
- Simulations show good estimation performance with low communication overhead.

## Abstract

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.