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Volume 11 Issue 7
Jul.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Contents
W. Song, Z. Wang, Z. Li, J. Wang, and  Q.-L. Han,  “Nonlinear filtering with sample-based approximation under constrained communication: Progress, insights and trends,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1539–1556, Jul. 2024. doi: 10.1109/JAS.2023.123588
Citation: W. Song, Z. Wang, Z. Li, J. Wang, and  Q.-L. Han,  “Nonlinear filtering with sample-based approximation under constrained communication: Progress, insights and trends,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1539–1556, Jul. 2024. doi: 10.1109/JAS.2023.123588

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

doi: 10.1109/JAS.2023.123588
Funds:  This work was supported in part by the National Key R&D Program of China (2022ZD0116401, 2022ZD0116400), the National Natural Science Foundation of China (62203016, U2241214, T2121002, 62373008, 61933007), the China Postdoctoral Science Foundation (2021TQ0009), the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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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 networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges 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 investigations have been launched towards the networked nonlinear filtering 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.


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    • Offer a systematic, concise and accessible review on the latest developments of the sample-based networked nonlinear filtering schemes under communication constraints
    • Summarize the up-to-date results from three engineering-oriented aspects, namely, incomplete/imperfect information compensation, resource saving, and security preservation
    • Point out some challenging and promising directions to facilitate the future research


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