扩展卡尔曼滤波器
控制理论(社会学)
卡尔曼滤波器
计算机科学
协方差
不变扩展卡尔曼滤波器
非线性系统
上下界
滤波器(信号处理)
节点(物理)
计算
事件(粒子物理)
数学
算法
工程类
控制(管理)
人工智能
统计
数学分析
物理
结构工程
量子力学
计算机视觉
作者
Hossein Rezaei,Majid Ghorbani
摘要
Abstract This study investigates the event‐triggered resilient recursive distributed state estimation problem for discrete‐time nonlinear systems over sensor networks. An event‐triggered mechanism is employed to save the limited computation resource and network bandwidth while maintaining the desired performance. A resilient Extended Kalman Filter (EKF) with consensus on estimations is developed, and consensus is first achieved with respect to the prediction estimation. The accuracy of the computed estimation is then improved via two recursive equations. By adopting the structure of the EKF, the filter gains are determined in each sensor node via utilization of an upper bound for the cross‐covariance, thereby resulting in a lower computational burden. The boundedness of the estimation errors is analyzed. Simulation results are reported to illustrate the effectiveness of the proposed distributed filter.
科研通智能强力驱动
Strongly Powered by AbleSci AI