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Cited by 13Target Tracking and Data Fusion in Sensor NetworksStability and Control of Uncertain SystemsControl Systems and Identification

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Recursive state estimation for state-saturated systems with two groups of measurements: Handling delayed and degraded sensors

Wen, Chuanbo, Wang, Zidong, Yang, Junjie, Ma, Lifeng·2023·Information Fusion
DOI 10.1016/j.inffus.2023.101814
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  1. Li, 2022, Robust set-membership filtering for two-dimensional systems with sensor saturation under the round-robin protocol, Internat. J. Systems Sci., 53, 2773, 10.1080/00207721.2022.2049918 · DOI
  2. Cattivelli, 2010, Diffusion strategies for distributed Kalman filtering and smoothing, IEEE Trans. Automat. Control, 55, 2069, 10.1109/TAC.2010.2042987 · DOI
  3. Xu, 2018, Finite-time distributed state estimation over sensor networks with round-robin protocol and fading channels, IEEE Trans. Cybern., 48, 336, 10.1109/TCYB.2016.2635122 · DOI
  4. Ge, 2022, Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks, IEEE/CAA J. Autom. Sin., 9, 31, 10.1109/JAS.2021.1004060 · DOI
  5. Sun, 2022, Finite-time distributed resilient state estimation subject to hybrid cyber-attacks: A new dynamic event-triggered case, Internat. J. Systems Sci., 53, 2832, 10.1080/00207721.2022.2083256 · DOI
  6. Caballero-Águilaa, 2015, Optimal state estimation for networked systems with random parameter matrices, correlated noises and delayed measurements, Int. J. Gen. Syst., 44, 142, 10.1080/03081079.2014.973728 · DOI
  7. Zhao, 2022, Unbiasedness-constrained least squares state estimation for time-varying systems with missing measurements under round-robin protocol, Internat. J. Systems Sci., 53, 1925, 10.1080/00207721.2022.2031338 · DOI
  8. Qu, 2022, Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: A torus-event-triggering mechanism, Internat. J. Systems Sci., 53, 1288, 10.1080/00207721.2021.1998721 · DOI
  9. Ciuonzo, 2017, On multiple covariance equality testing with application to SAR change detection, IEEE Trans. Signal Process., 65, 5078, 10.1109/TSP.2017.2712124 · DOI
  10. Geng, 2021, Multi-sensor filtering fusion meets censored measurements under a constrained network environment: Advances, challenges and prospects, Internat. J. Systems Sci., 52, 3410, 10.1080/00207721.2021.2005178 · DOI
  11. Caballero-Águila, 2020, Networked fusion estimation with multiple uncertainties and time-correlated channel noise, Inf. Fusion, 54, 161, 10.1016/j.inffus.2019.07.008 · DOI
  12. Shmaliy, 2017, Unbiased finite impluse response filtering: an iterative alternative to Kalman filtering ignoring noise and initial conditions, IEEE Control Syst. Mag., 37, 70, 10.1109/MCS.2017.2718830 · DOI
  13. Chen, 2014, Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks, IEEE Trans. Signal Process., 62, 797, 10.1109/TSP.2013.2294603 · DOI
  14. Meng, 2022, Protocol-based variance-constrained distributed secure filtering with measurement censoring, Internat. J. Systems Sci., 53, 3322, 10.1080/00207721.2022.2080297 · DOI
  15. Gao, 2021, Dynamic-transmission-based recursive filtering algorithm for microseismic event detection under sensor saturations, Measurement, 186, 10.1016/j.measurement.2021.110197 · DOI
  16. Suo, 2021, Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations, Neurocomputing, 455, 297, 10.1016/j.neucom.2021.01.131 · DOI
  17. Li, 2022, Event-triggered sliding mode control for multi-agent systems subject to channel fading, Internat. J. Systems Sci., 53, 1233, 10.1080/00207721.2021.1995527 · DOI
  18. Wen, 2018, Event-based distributed recursive filtering for state-saturated systems with redundant channels, Inf. Fusion, 39, 96, 10.1016/j.inffus.2017.04.004 · DOI
  19. Ju, 2021, Finite-horizon H∞ filtering and fault isolation for a class of time-varying systems with sensor saturation, Internat. J. Systems Sci., 52, 321, 10.1080/00207721.2020.1829160 · DOI
  20. Yan, 2016, H∞ Filtering for nonlinear networked systems with randomly occuring distributed delays missing measurements and sensor saturation, Inform. Sci., 370–371, 772, 10.1016/j.ins.2015.09.027 · DOI
  21. Men, 2018, Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations, Neurocomputing, 291, 207, 10.1016/j.neucom.2018.02.071 · DOI
  22. Li, 2021, Distributed filtering for delayed nonlinear system with random sensor saturation: A dynamic event-triggered approach, Syst. Sci. Control Eng., 9, 440, 10.1080/21642583.2021.1919935 · DOI
  23. Sheng, 2017, H∞ Filtering for T-S fuzzy complex networks subject to sensor saturation via delayed information, IET Control Theory Appl., 11, 2370, 10.1049/iet-cta.2017.0071 · DOI
  24. Yan, 2019, Event-triggered set-membership filtering for discrete-time memristive neural networks subject to measurement saturation and fadings, Neurocomputing, 346, 20, 10.1016/j.neucom.2018.07.088 · DOI
  25. Chen, 2022, Dynamic anti-windup design for linear systems with time-varying state delay and input saturations, Internat. J. Systems Sci., 53, 2165, 10.1080/00207721.2022.2043483 · DOI
  26. Wang, 2021, Neural-network-based control for discrete-time nonlinear systems with input saturation under stochastic communication protocol, IEEE/CAA J. Autom. Sin., 8, 766, 10.1109/JAS.2021.1003922 · DOI
  27. Zhang, 2021, Fault-tolerant optimal control for discrete-time nonlinear system subjected to input saturation: A dynamic event-triggered approach, IEEE Trans. Cybern., 51, 2956, 10.1109/TCYB.2019.2923011 · DOI
  28. Wang, 2021, NN-based adaptive tracking control of discrete-time nonlinear systems with actuator saturation and event-triggering protocol, IEEE Trans. Syst. Man Cybern., 51, 7613, 10.1109/TSMC.2020.2981954 · DOI
  29. Yin, 2016, Adaptive periodic event-triggered consensus for multi-agent systems subject to input saturation, Internat. J. Control, 89, 653, 10.1080/00207179.2015.1088967 · DOI
  30. Selvaraj, 2019, Observer-based synchronization of complex dynamical networks under actuator saturation and probabilistic faults, IEEE Trans. Syst. Man Cybern., 496, 1516, 10.1109/TSMC.2018.2803261 · DOI
  31. Chen, 2020, Adaptive neural finite-time bipartite consensus tracking of nonstrict feedback nonlinear coopetition multi-agent systems with input saturation, Neurocomputing, 397, 168, 10.1016/j.neucom.2020.02.054 · DOI
  32. Zhou, 2020, Event-triggered finite-time H∞ control of networked state-saturated switched systems, Internat. J. Systems Sci., 51, 1744, 10.1080/00207721.2020.1777344 · DOI
  33. Ding, 2013, State-saturated H∞ filtering with randomly occurring nonlinearities and packet dropouts: the finite-horizon case, Internat. J. Robust Nonlinear Control, 23, 1803, 10.1002/rnc.2850 · DOI
  34. Zhang, 2021, Event-triggered filter design of positive systems with state saturation, IEEE Syst. J., 15, 4281, 10.1109/JSYST.2020.3019489 · DOI
  35. Agarwal, 2022, Novel criterion for preventing overflow oscillations in fixed-point digital filters with state saturation, IEEE Signal Process. Lett., 29, 1287, 10.1109/LSP.2022.3176489 · DOI
  36. Shen, 2020, Distributed state-saturated recursive filtering over sensor networks under round-robin protocol, IEEE Trans. Cybern., 50, 3605, 10.1109/TCYB.2019.2932460 · DOI
  37. Wen, 2018, Recursive distributed filtering for a class of state-saturated systems with fading measurements and quantization effects, IEEE Trans. Syst. Man Cybern., 48, 930, 10.1109/TSMC.2016.2629464 · DOI
  38. Han, 2016, Optimal communication network-based H∞ quantized control with packet dropouts for a class of discrete-time neural networks with distributed time delay, IEEE Trans. Neural Netw. Learn. Syst., 27, 426, 10.1109/TNNLS.2015.2411290 · DOI
  39. Sun, 2017, Multi-sensor distributed fusion estimation with applications in networked systems: a review paper, Inf. Fusion, 38, 122, 10.1016/j.inffus.2017.03.006 · DOI
  40. Zhang, 2004, A reorganized innovation approach to linear estimation, IEEE Trans. Automat. Control, 49, 1810, 10.1109/TAC.2004.835599 · DOI
  41. Zhang, 2018, H∞ Filtering for discrete-time switched fuzzy systems with randomly occurring time-varying delay and packet dropouts, Signal Process., 143, 320, 10.1016/j.sigpro.2017.09.009 · DOI
  42. Hu, 2018, Joint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations, Automatica, 97, 150, 10.1016/j.automatica.2018.07.027 · DOI
  43. Guan, 2019, Finite-time H∞ filtering for discrete-time singular Markovian jump systems with time delay and input saturation, Complexity, 2019, 10.1155/2019/4675397 · DOI
  44. Zhang, 2015, Energy efficient distributed filtering for a class of nonlinear systems in sensor networks, IEEE Sens. J., 15, 3026, 10.1109/JSEN.2014.2386348 · DOI
  45. Hu, 2012, Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements, Automatica, 48, 2007, 10.1016/j.automatica.2012.03.027 · DOI
  46. Kai, 2010, Robust extended Kalman filtering for nonlinear systems with stochastic uncertainties, IEEE Trans. Syst. Man Cybern. A, 40, 399, 10.1109/TSMCA.2009.2034836 · DOI
  47. Wang, 1992, Robust control of a class of uncertain nonlinear systems, Systems Control Lett., 19, 139, 10.1016/0167-6911(92)90097-C · DOI
  48. Jin, 2022, Distributed Kalman filtering for sensor networks with random sensor activation, delays, and packet dropouts, Internat. J. Systems Sci., 53, 575, 10.1080/00207721.2021.1963502 · DOI
  49. Sun, 2013, Optimal linear filters for discrete time systems with randomly delayed and lost measurements with/without time stamps, IEEE Trans. Automat. Control, 58, 1551, 10.1109/TAC.2012.2229812 · DOI
  50. Chu, 2021, Consensus of multiagent systems with relative state saturations, IEEE Trans. Syst. Man Cybern., 51, 2391, 10.1109/TSMC.2019.2912980 · DOI

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Metadata

Authors
Wen, C., Wang, Z., Yang, J., & Ma, L.
Affiliations
  • Shanghai Dianji University (CN)
  • Brunel University of London (GB)
  • Nanjing University of Science and Technology (CN)
Corresponding author
Wen, Chuanbo
ORCID
Venue
Information Fusion
Publisher
Elsevier BV
Published
2023-09
Volume
97
Pages
101814
ISSN
1566-2535 (ISSN-L)
DOI
10.1016/j.inffus.2023.101814
OpenAlex
W4366753657
Cited by
13
References
0
Citations by year
2026: 1 · 2025: 5 · 2024: 6 · 2023: 1
FWCI
1.15
Type
journal article
Language
en
Open access
closed
Licence
Locations
Information Fusion (publishedVersion)
Funders
  • Alexander von Humboldt-Stiftung
  • Royal Society
  • National Natural Science Foundation of China (61933007, 61973209, 62273180)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords
recursive state estimation, sensor fusion, delayed measurements, measurement delay, estimator gain design

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