A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 3 Issue 2
Apr.  2016

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Shiming Chen, Xiaoling Chen, Zhengkai Pei, Xingxing Zhang and Huajing Fang, "Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 225-232, 2016.
Citation: Shiming Chen, Xiaoling Chen, Zhengkai Pei, Xingxing Zhang and Huajing Fang, "Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 225-232, 2016.

Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics


This work was supported by National Natural Science Foundation of China (61364017,60804066), The Scientific and Technological Project of Education Department of Jiangxi Province (KJLD12068), and Natural Science Foundation of Jiangxi Province (20132BAB201039).

  • Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm.


  • loading
  • [1]
    Mahfouz S, Mourad-Chehade F, Honeine P, Farah J, Sonussi H. Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sensors Journal, 2014, 14(10): 3715-3725
    Alzaq H, Kabadayi S. Mobile robot comes to the rescue in a WSN. In: Proceedings of the 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC). London, England: IEEE, 2013. 1977-1982
    Alippi C, Camplani R, Galperti C, Roveri M. A robust, adaptive, solarpowered WSN framework for aquatic environmental monitoring. IEEE Sensors Journal, 2011, 11(1): 45-55
    Chen B, Zhang W A, Yu L. Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks. IEEE Transactions on Signal Processing, 2014, 62(4): 797-812
    Xu J, Song E B, Luo Y T, Zhu Y M. Optimal distributed kalman filtering fusion algorithm without invertibility of estimation error and sensor noise covariances. IEEE Signal Processing Letters, 2012, 19(1): 55-58
    Lynch N A. Distributed Algorithms. San Francisco, CA: Morgan Kaufmann, 1997. 372-391
    Olfati-Saber R. Kalman-consensus filter: optimality, stability, and performance. In: Proceedings of the 48th IEEE Conference on Decision and Control. Shanghai, China: IEEE, 2009. 7036-7042
    Wang Shuai, Yang Wen, Shi Hong-Bo. Consensus-based filtering algorithm with packet-dropping. Acta Automatica Sinica, 2010, 36(12): 1689 -1696 (in Chinese)
    Olfati-Saber R, Jalalkamali P. Collaborative target tracking using distributed Kalman filtering on mobile sensor networks. In: Proceedings of the 2011 American Control Conference. San Francisco, America: IEEE, 2011. 1100-1105
    Matei I, Baras J S. Consensus-based linear distributed filtering. Automatica, 2012, 48(8): 1776-1782
    Xi Feng, Liu Zhong. Distributed Kalman filter with information matrix weighted consensus strategies. Information and Control, 2010, 39(2): 194-199 (in Chinese)
    Chen Shi-Ming, Wu Long-Long, Ding Xian-Da, Fang Hua-Jing. Consensus-based Kalman filtering algorithm based on weighted quantitative uncertainty. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2013, 41(3): 30-33 (in Chinese)
    Kingston D B, Ren W, Beard R W. Consensus algorithms are input-tostate stable. In: Proceedings of the 2005 American Control Conference. Portland, USA: IEEE, 2005. 1686-1690
    Zhang Yong-Gang, Wang Cheng-Cheng, Wei Ye, Li Ning, Zhou Wei- Dong. A spatially distributed variable tap-length strategy over adaptive networks. Acta Automatica Sinica, 2014, 40(7): 1355-1365 (in Chinese)
    Jakoveti'c D, Xavier J, Moura J M F. Weight optimization for consensus algorithms with correlated switching topology. IEEE Transactions on Signal Processing, 2010, 58(7): 3788-3801
    Duan Dong-Li, Wu Xiao-Yue. Cascading failure of scale-free networks based on a tunable load redistribution model. Acta Physica Sinica, 2014, 63(3): 30501 (in Chinese)
    Lehmann J, Bernasconi J. Stochastic load-redistribution model for cascading failure propagation. Physical Review E, 2010, 81(3): 031229
    Ribeiro A, Giannakis G B, Roumeliotis S I. SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Transactions on Signal Processing, 2006, 54(12): 4782-4795
    Xiao L, Boyd S. Fast linear iterations for distributed averaging. Systems and Control Letters, 2004, 53(1): 65-78
    Zhang F Z. The Schur Complement and Its Applications. New York: Springer-Verlag, 2005. 2-15


    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

    Article Metrics

    Article views (1008) PDF downloads(1) Cited by()


    DownLoad:  Full-Size Img  PowerPoint