首页|Nonlinear Filtering With Sample-Based Approxi-mation Under Constrained Communication:Progress,Insights and Trends

Nonlinear Filtering With Sample-Based Approxi-mation Under Constrained Communication:Progress,Insights and Trends

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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements.In recent years,the advance of network communication technology has not only popularized the net-worked systems with apparent advantages in terms of installation,cost and maintenance,but also brought about a series of chal-lenges to the design of nonlinear filtering algorithms,among which the communication constraint has been recognized as a dominating concern.In this context,a great number of investiga-tions have been launched towards the networked nonlinear filter-ing problem with communication constraints,and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios.The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints.More specifically,we first review three important families of sample-based filtering methods known as the unscented Kalman filter,particle filter,and maximum correntropy filter.Then,the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information,limited resources and cyber security.Finally,several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

Communication constraintsmaximum correntropy filternetworked nonlinear filteringparticle filtersample-based approximationunscented Kalman filter

Weihao Song、Zidong Wang、Zhongkui Li、Jianan Wang、Qing-Long Han

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State Key Laboratory for Turbulence and Complex Systems,Department of Mechanics and Engineering Science,College of Engineering,Peking University,Beijing 100871,China

Department of Computer Science,Brunel University London,Uxbridge,Middlesex,UB8 3PH,United Kingdom

School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China

School of Science,Computing and Engineering Technologies,Swinburne University of Technology,Melbourne,VIC 3122,Australia

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National Key R&D Program of ChinaNational Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationRoyal Society of the UKAlexander von Humboldt Foundation of Germany

2022ZD01164012022ZD011640062203016U2241214T212100262373008619330072021TQ0009

2024

10.1109/JAS.2023.123588

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(7)