Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method
针对带宽有限的无线网络，提出一种基于最小二乘的事件触发机制，并设计箱粒子滤波算法实现非线性非高斯系统的远程状态估计，降低计算复杂度且不依赖高斯假设。
This article is concerned with the problem of event-based remote state estimation for nonlinear/non-Gaussian systems on a wireless network with limited bandwidth. To reduce unnecessary data transmissions, a novel event-triggering mechanism is developed by using the least-square technique. Based on this, an event-triggered box particle filtering scheme is designed to realize the minimum mean-squared error estimation at the remote estimator end, in which the posterior probability density functions are calculated separately according to the information of the event-triggered indicator to avoid the problem of excessive estimation error. Different from the existing approaches, the proposed algorithm does not depend on any Gaussian assumptions and reduces the computational complexity under the premise of ensuring the estimation performance. Finally, two simulation examples are performed to demonstrate the validity of the proposed algorithm.