A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
B. Sun and X. Cao, “Optimal sensor scheduling for remote state estimation with partial channel observation,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125180
Citation: B. Sun and X. Cao, “Optimal sensor scheduling for remote state estimation with partial channel observation,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125180

Optimal Sensor Scheduling for Remote State Estimation With Partial Channel Observation

doi: 10.1109/JAS.2025.125180
More Information
  • loading
  • [1]
    Y. Liu, Y. Peng, B. Wang, S. Yao, and Z. Liu, “Review on cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 27–40, 2017. doi: 10.1109/JAS.2017.7510349
    [2]
    S. Zhang, L. Peng, and X. Chang, “Optimal energy allocation based on SINR under DoS attack,” Neurocomputing, vol. 570, p. 127116, 2024. doi: 10.1016/j.neucom.2023.127116
    [3]
    L. Schenato, B. Sinopoli, M. Franceschetti, K. Poolla, and S. S. Sastry, “Foundations of control and estimation over lossy networks,” Proc. IEEE, vol. 95, no. 1, pp. 163–187, 2007. doi: 10.1109/JPROC.2006.887306
    [4]
    M. Huang and S. Dey, “Stability of Kalman filtering with Markovian packet losses,” Automatica, vol. 43, no. 4, pp. 598–607, 2007. doi: 10.1016/j.automatica.2006.10.023
    [5]
    S. Wu, X. Ren, S. Dey, and L. Shi, “Optimal scheduling of multiple sensors over shared channels with packet transmission constraint,” Automatica, vol. 96, pp. 22–31, 2018. doi: 10.1016/j.automatica.2018.06.019
    [6]
    X. Cao, P. Cheng, J. Chen, S. S. Ge, Y. Cheng, and Y. Sun, “Cognitive radio based state estimation in cyber-physical systems,” IEEE J. Sel. Areas Commun., vol. 32, no. 3, pp. 489–502, 2014. doi: 10.1109/JSAC.2014.1403002
    [7]
    V. K. Lau and Y.-K. R. Kwok, Channel-Adaptive Technologies and Crosslayer Designs for Wireless Systems With Multiple Antennas: Theory and Applications. New York, USA: John Wiley & Sons, 2006.
    [8]
    A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,” IEEE Commun. Mag., vol. 46, no. 4, pp. 32–39, 2008. doi: 10.1109/MCOM.2008.4481338
    [9]
    B. Sun, X. Cao, L. Wang, and C. Sun, “Optimal online transmission schedule for remote state estimation over a hidden Markovian channel,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 2519–2525, 2020. doi: 10.1016/j.ifacol.2020.12.228
    [10]
    S. Wu, X. Ren, Q.-S. Jia, K. H. Johansson, and L. Shi, “Learning optimal scheduling policy for remote state estimation under uncertain channel condition,” IEEE Trans. Control Netw. Syst., vol. 7, no. 2, pp. 579–591, 2019.
    [11]
    L. Shi, P. Cheng, and J. Chen, “Sensor data scheduling for optimal state estimation with communication energy constraint,” Automatica, vol. 47, no. 8, pp. 1693–1698, 2011. doi: 10.1016/j.automatica.2011.02.037
    [12]
    B. D. Anderson and J. B. Moore, Optimal Filtering. North Chelmsford, USA: Courier Corporation, 2005.
    [13]
    D. A. McAllester and S. Singh, “Approximate planning for factored POMDPs using belief state simplification,” arXiv preprint arXiv: 1301.6719, 2013.
    [14]
    C. Boutilier, “A pomdp formulation of preference elicitation problems,” in Proc. AAAI/IAAI, AB, 2002, pp. 239–246.
    [15]
    V. Krishnamurthy and D. V. Djonin, “Structured threshold policies for dynamic sensor scheduling—A partially observed Markov decision process approach,” IEEE Trans. Signal Process., vol. 55, no. 10, pp. 4938–4957, 2007. doi: 10.1109/TSP.2007.897908
    [16]
    V. Krishnamurthy, Partially Observed Markov Decision Processes. Cambridge, UK: Cambridge university press, 2016.
    [17]
    D. Bertsekas, Abstract Dynamic Programming. Athena Scientific, 2022.
    [18]
    W. S. Lovejoy, “Computationally feasible bounds for partially observed Markov decision processes,” Oper. Res., vol. 39, no. 1, pp. 162–175, 1991. doi: 10.1287/opre.39.1.162
    [19]
    J. Pineau, G. Gordon, S. Thrun et al., “Point-based value iteration: An anytime algorithm for pomdps,” in Proc. IJCAI, 2003, vol. 3, pp. 1025–1032.
    [20]
    H. Liu, Y. Li, K. H. Johansson, J. Martensson, and L. Xie, “Rollout approach to sensor scheduling for remote state estimation under integrity attack,” Automatica, vol. 144, p. 110473, 2022. doi: 10.1016/j.automatica.2022.110473

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)

    Article Metrics

    Article views (21) PDF downloads(14) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return