Recursive filtering of networked nonlinear systems: a survey

卡尔曼滤波器 计算机科学 非线性系统 高斯分布 领域(数学) 脆弱性(计算) 人工智能 数学 计算机安全 量子力学 物理 纯数学
作者
Jingyang Mao,Ying Sun,Xiaojian Yi,Hongjian Liu,Derui Ding
出处
期刊:International Journal of Systems Science [Taylor & Francis]
卷期号:52 (6): 1110-1128 被引量:202
标识
DOI:10.1080/00207721.2020.1868615
摘要

Recursive filtering for nonlinear systems, one of the core technologies of modern industrial systems, is an ever-increasing research topic from the control and computer communities. Some challenges from communication scheduling, limited bandwidth as well as security vulnerability have to be seriously handled though the applications of communication technologies bring into some conveniences. As such, it is of utmost significance in theory and great importance in applications to establish engineering-feasible recursive filtering algorithms for networked nonlinear systems. This paper focuses on the development of this topic and provides an up-to-date survey of the existing nonlinear filtering techniques. The introduction of three classes of communication protocols is first presented in great detail, and then comprehensive reviews and summaries of the nonlinear recursive filtering problems with Gaussian/non-Gaussian noises are elaborated according to different strategies responding to nonlinear functions or noises. Particularly, the reviews are layout from the extended Kalman filtering, the unscented/cubature Kalman filtering, the set-membership filtering as well as the H∞ filtering. Furthermore, several challenging issues are raised to stimulate further related theoretical research and practical applications in this field.

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