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 1 Issue 2
Apr.  2014

IEEE/CAA Journal of Automatica Sinica

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Haiyang Yu, Yisha Liu and Wei Wang, "Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 149-154, 2014.
Citation: Haiyang Yu, Yisha Liu and Wei Wang, "Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 149-154, 2014.

Distributed Sparse Signal Estimation in Sensor Networks Using H-Consensus Filtering

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This work was supported by National Natural Science Foundation of China (61305128).

  • This paper is concerned with the sparse signal recovery problem in sensor networks, and the main purpose is to design a filter for each sensor node to estimate a sparse signal sequence using the measurements distributed over the whole network. A so-called l1-regularized H filter is established at first by introducing a pseudo-measurement equation, and the necessary and sufficient condition for existence of this filter is derived by means of Krein space Kalman filtering. By embedding a high-pass consensus filter into l1-regularized H filter in information form, a distributed filtering algorithm is developed, which ensures that all node filters can reach a consensus on the estimates of sparse signals asymptotically and satisfy the prescribed H performance constraint. Finally, a numerical example is provided to demonstrate effectiveness and applicability of the proposed method.

     

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  • [1]
    Candes E J, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2):489-509
    [2]
    Candes E J, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, 59(8):1207-1223
    [3]
    Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2):21-30
    [4]
    Vaswani D. Kalman filtered compressed sensing. In:Proceedings of the 15th IEEE International Conference on Image Processing. San Diego, USA:IEEE, 2008. 893-896
    [5]
    Charles A S, Rozell C J. Dynamic filtering of sparse signals using reweighted l1. In:Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing. Vancouver, Canada:IEEE, 2013. 1-5
    [6]
    Carmi A, Gurfil P, Kanevsky D. Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms. IEEE Transactions on Signal Processing, 2010, 58(4):2405-2409
    [7]
    Kanevsky D, Carmi A, Horesh L. Kalman filtering for compressed sensing. In:Proceedings of the 13th Conference on Information Fusion (FUSION). Edinburgh, UK:IEEE, 2010. 1-8
    [8]
    Wan Yi-Ming, Dong Wei, Ye Hao. Distributed H filtering with consensus strategies in sensor networks:considering consensus tracking error. Acat Automatica Sinica, 2012, 38(7):1211-1217(in Chinese)
    [9]
    Wang Shuai, Yang Wen, Shi Hong-Bo. Consensus-based filtering algorithm with packet-dropping. Acat Automatica Sinica, 2010, 36(12):1689-1696(in Chinese)
    [10]
    Feng Xiao-Liang, Wen Cheng-Lin, Liu Wei-Feng, Li Xiao-Fang, Xu Li-Zhong. Sequential fusion finite horizon H filtering for Multisenor System. Acat Automatica Sinica, 2013, 39(9):1523-1532(in Chinese)
    [11]
    Olfati-Saber R. Distributed Kalman filter in sensor networks. In:Proceedings of the 46th IEEE Conference on Decision and Control. Los Angeles, New Orleans, LA:IEEE, 2007. 5492-5498
    [12]
    Olfati-Saber R. Kalman-consensus filter:optimality, stability, and performance. In:Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference. Shanghai, China:IEEE, 2009. 7036-7042
    [13]
    Shen B, Wang Z D, Hung Y S, Chesi G. Distributed H filtering for polynomial nonlinear stochastic systems in sensor networks. IEEE Transactions on Industrial Electronics, 2011, 58(5):1971-1979
    [14]
    Ugrinovskii V. Distributed robust filtering with H consensus of estimates. Automatica, 2011, 47(1):1-13
    [15]
    Hassibi B, Sayed A H, Kailath T. Indefinite Quadratic Estimation and Control:a Unified Approach to H2 and H Theories. Philadelphia:SIAM, 1999.

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