论文标题
随机事件触发的变异贝叶斯过滤
Stochastic Event-triggered Variational Bayesian Filtering
论文作者
论文摘要
本文提出了一个事件触发的变异贝叶斯过滤器，用于远程状态估计，并具有未知和时变噪声协方差。在预设了多个名义过程噪声协方差和初始测量噪声协方差之后，使用变异的贝叶斯方法和定点迭代方法在随机事件触发的机制下共同估计后矢量和未知的噪声协方差。提出的算法可确保对各种未知噪声协方差范围的沟通负荷和出色的估计性能。最后，通过跟踪车辆的模拟来证明所提出的算法的性能。
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.
