Stochastic Event-triggered Variational Bayesian Filtering

TL;DR

This paper introduces a stochastic event-triggered variational Bayesian filter that efficiently estimates states and unknown noise covariances in remote sensing, reducing communication load while maintaining high accuracy.

Contribution

It presents a novel variational Bayesian filtering approach with event-triggered mechanism for joint state and noise covariance estimation in uncertain environments.

Findings

Low communication loads achieved

High estimation accuracy demonstrated

Effective in vehicle tracking simulations

Abstract

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.