Cheng Guorui, Liu Jingang, Song Shenmin
Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2024 Jan 24;24(3):769. doi: 10.3390/s24030769.
This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical-radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.
本文首先探讨非线性系统中事件触发状态估计面临的挑战，应对数据包丢失和相关噪声问题。引入一种通信机制，该机制根据事件触发条件是否被违反来决定是否传输测量值，从而最小化冗余通信数据。在设计滤波器时，首先进行噪声去相关，然后将事件触发机制与不可靠网络传输系统集成以开发状态估计器。随后，结合三阶球径向容积规则，概述了所提出的状态估计框架的数值实现步骤。性能估计分析表明，通过适当调整事件触发阈值，可以有效平衡估计性能和传输速率。结果表明，当数据包丢失率存在下限，状态估计误差的协方差矩阵保持有界，并且状态估计误差的随机稳定性也得到了证实。最后，通过目标跟踪系统的仿真示例验证了本文提出的算法和结论。
