事件触发调度下带有动态偏差的传感器 网络分布式融合状态估计
作者单位:

吕梁学院

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Distributed Fusion State Estimation of Sensor Networks with Dynamic Bias under Event-Triggered Scheduling
Affiliation:

LYULIANG UNIVERSITY

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    摘要:本文研究了一类基于传感器网路传输的具有加性和乘性噪声的线性系统的事件触发分布式滤波问题, 并且所考虑的过程噪声和测量噪声具有一步自相关且两步交叉相关特性. 首先, 利用一个递推方程描述系统的动态偏差, 并采用伯努利分布的随机变量刻画随机丢包现象. 其次, 引入事件触发机制在确保滤波性能的前提下降低信息传输频率, 构造基于一致性的新型分布式滤波器. 再次, 利用随机分析技术建立滤波误差协方差上界的递推方程并通过最小化方差约束指标, 给出滤波增益的表达式. 最后, 通过数值仿真验证了所提出的优化滤波方法的有效性.

    Abstract:

    Abstract Based on sensor network transmission, the event-triggered distributed filtering problem for a class of linear systems with additive and multiplicative noises is investigated in this paper, in which the considered process noise and measurement noise have one-step autocorrelation and two-step cross-correlation characteristics. Firstly, a recursive equation is used to describe the dynamic deviation of the system and the random variables of the Bernoulli distribution are introduced to describe the random packet loss phenomenon.Secondly, an event-triggered mechanism is introduced to reduce the frequency of information transmission while ensuring filtering performance, and a novel distributed filter is constructed based on consistency.? Then, a recursive equation for the upper bound of filtering error covariance is established using random analysis technology, and an expression for filtering gain is given by minimizing the variance constraint index. Finally, the effectiveness of the proposed optimized filtering method was verified through numerical simulation.

    参考文献
    [1] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Computer Networks, vol. 52, no. 12, pp. 2292-2330, 2008.
    [2] J. C. Das, D. De, S. P. Mondal, A. Ahmadian, F. Ghaemi, and N. Senu, “QCA based error detection circuit for nano communication network,” IEEE Access, vol. 7, pp. 67355-67366, 2019.
    [3] M. Adeli, M. Hajatipour, M. J. Yazdanpanah, M. Sha?eirad, and H. Hashemi-Dezaki, “Distribut- ed trust-based unscented Kalman ?lter for non-linear state estimation under cyber-attacks: the application of manoeuvring target tracking over wireless sensor networks,” IET Control Theory & Applications, vol. 15, no. 15, pp. 1987-1998, 2021.
    [4] B. Chen, W.-A. Zhang, and L. Yu, “Distributed ?nite-horizon fusion Kalman ?ltering for band- width and energy constrained wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 62, no. 4, pp. 797-812, 2014.
    [5] J. Feng, Z. Wang, and M. Zeng, “Distributed weighted robust Kalman ?lter fusion for uncertain systems with autocorrelated and cross-correlated noises,” Information Fusion, vol. 14, no. 1, pp. 78-86, 2013.
    [6] D. Ding, Z. Wang, D. W. C. Ho, and G. Wei, “Distributed recursive ?ltering for stochastic systems under uniform quantizations and deception attacks through sensor networks,” Automatica, vol. 78, pp. 231–240, 2017.
    [7] V. Dragan, “Optimal ?ltering for discrete-time linear systems with multiplicative white noise perturbations and periodic coe?cients,” IEEE Transactions on Automatic Control, vol. 58, no. 4, pp. 1029-1034, 2013.
    [8] W. Li, Y. Jia, and J. Du, “Resilient ?ltering for nonlinear complex networks with multiplicative noise,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2522-2528, 2019.
    [9] M. B. Ignagni, “Separate bias Kalman estimator with bias state noise,” IEEE Transactions on Automatic Control, vol. 35, no. 3, pp. 338-341, 1990.
    [10] J. Hu, Z. Wang, and G. P. Liu, “Delay compensation-based state estimation for time-varying complex networks with incomplete observations and dynamical bias,” IEEE Transactions Cyber- netics, vol. 52, no. 11, pp. 12071-12083, 2022.
    [11] Y. Shen, Z. Wang, and H. Dong, “Minimum-variance state and fault estimation for multirate systems with dynamical bias,” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 69, no. 4, pp. 2361-2365, 2022.
    [12] X. Ge, Q. L. Han, and Z. Wang, “A threshold-parameter-dependent approach to designing dis- tributed event-triggered Ho consensus ?lters over sensor networks,” IEEE Transactions Cyber- netics, vol. 49, no. 4, pp. 1148-1159, 2019.
    [13] Y. Liu, Z. Wang, L. Zou, J. Hu, and H. Dong, “Distributed ?ltering for complex networks under multiple event-triggered transmissions within node-wise communications,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2521-2534, 2022.
    [14] H. Yan, X. Xu, H. Zhang, and F. Yang, “Distributed event-triggered Ho state estimation for ToS fuzzy systems over ?ltering networks,” Journal of the Franklin Institute, vol. 354, no. 9, pp. 3760-3779, 2017.
    [15] Q. Li, B. Shen, Z. Wang, and W. Sheng, “Recursive distributed ?ltering over sensor networks on GilbertoElliott channels: a dynamic event-triggered approach,” Automatica, vol. 113, 2020.
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王有刚.事件触发调度下带有动态偏差的传感器 网络分布式融合状态估计[J].南京信息工程大学学报,,():

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  • 收稿日期:2023-05-23
  • 最后修改日期:2023-10-07
  • 录用日期:2023-10-11

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