传感器网络中带有一致性策略的分布式<I>H</I><SUB>∞</SUB>滤波:考虑一致性跟踪误差

万一鸣; 董炜; 叶昊

doi:10.3724/SP.J.1004.2012.01211

传感器网络中带有一致性策略的分布式H_∞滤波:考虑一致性跟踪误差

doi: 10.3724/SP.J.1004.2012.01211 cstr: 32138.14.SP.J.1004.2012.01211

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出版历程
- 收稿日期: 2010-07-19
- 修回日期: 2011-04-12
- 刊出日期: 2012-07-20

Distributed H_∞ Filtering with Consensus Strategies in Sensor Networks: Considering Consensus Tracking Error

摘要

摘要: 现有的带有一致性策略的分布式滤波方法包含两个步骤：与相邻传感器节点交互通信的一致性步骤, 以及本地滤波步骤. 本文分析了一致性跟踪误差对于本地估计误差的影响, 并针对此影响, 提出了新的分布式H∞滤波方法. 当采样周期中一致性迭代次数有限时, 本文提出的方法能够抑制一致性跟踪误差对本地估计误差的影响；当采样周期中一致性迭代次数趋于无穷, 即一致性跟踪误差收敛到零时, 本文提出的分布式算法中的本地滤波就等价于集中式滤波. 仿真表明了本文方法的有效性.
- 分布式H∞滤波 /
- 一致性跟踪误差
Abstract: The existing distributed H∞ filtering with consen-sus strategies consists of two steps: the consensus step through locally communicating with neighboring sensor nodes and the local filtering step. In this paper, the in°uence of consensus tracking error on the local estimation error is analyzed, and a distributed H∞ filtering algorithm considering the consensus tracking error is proposed. When the number of consensus iter-ations per sampling period is limited, the proposed method can suppress the effect of consensus tracking error on local estimation error; when the number of consensus iterations per sampling pe-riod goes to infinity, i.e., the consensus tracking error converges to zero, the local filtering in the distributed algorithm reduces to the centralized H∞ filtering. Simulation shows the effectiveness of the proposed method.
- Sensor networks /
- distributed H∞ filter

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Olfati-Saber R. Distributed Kalman filter with embedded consensus filters. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain: IEEE, 2005. 8179-8184[2] Olfati-Saber R. Distributed Kalman filtering for sensor networks. In: Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, USA: IEEE, 2007. 5492-5498[3] Kamgarpour M, Tomlin C. Convergence properties of a decentralized Kalman filter. In: Proceedings of the 47th IEEE Conference on Decision and Control. Cancun, Mexico: IEEE, 2008. 3205-3210[4] Casbeer D W, Beard R. Distributed information filtering using consensus filters. In: Proceedings of the American Control Conference. St. Louis, USA: IEEE, 2009. 1882-1887[5] Ahmad A, Gani M, Yang F. Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks. Signal Processing, 2008, 88(8): 1919-1928[6] Favero S D, Zampieri S. Distributed estimation through randomized gossip Kalman filter. In: Proceedings of the 48th IEEE Conference on Decision and Control and the 28th Chinese Control Conference. Shanghai, China: IEEE, 2009. 7049-7054[7] Stankovic S S, Stankovic M S, Stipanovic D M. A consensus based overlapping decentralized estimator in lossy networks: stability and denoising effects. In: Proceedings of the American Control Conference. Seattle, USA: IEEE, 2008. 4364-4369[8] Cortes J. Distributed Kriged Kalman filter for spatial estimation. IEEE Transactions on Automatic Control, 2009, 54(12): 2816-2827[9] Dall'Anese E, Kim S J, Giannakis G B. Channel gain map tracking via distributed Kriging. IEEE Transactions on Vehicular Technology, 2011, 60(3): 1205-1211[10] Khan U A, Moura J M F. Distributing the Kalman filter for large-scale systems. IEEE Transactions on Signal Processing, 2008, 56(10): 4919-4935[11] Cattivelli F S, Sayed A H. Diffusion strategies for distributed Kalman filtering and smoothing. IEEE Transactions on Automatic Control, 2010, 55(9): 2069-2084[12] Carli R, Chiuso A, Schenato L, Zampieri S. Distributed Kalman filtering based on consensus strategies. IEEE Journal on Selected Areas in Communications, 2008, 26(4): 622-633[13] Sun Shu-Li, Lv Nan. Distributed fusion filter for multi-sensor multi-delay systems with colored measurement noises. Acta Automatica Sinica, 2009, 35(1): 46-53 (in Chinese)[14] Xie S, Xie L, Rahardja S. An LMI-based decentralized H∞ filtering for interconnected linear systems. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington D. C., USA: IEEE, 2003. 589-592[15] Chen N, Zhang X, Gui W, Jiang Z. Delay-dependant decentralized H∞ filtering for uncertain interconnected systems. In: Proceedings of the IEEE International Conference on Control and Automation. Guangzhou, China: IEEE, 2007. 660-665[16] Zhang H, Dang C, Li C. Decentralized H∞ filter design for discrete-time interconnected fuzzy systems. IEEE Transactions on Fuzzy Systems, 2009, 17(6): 1428-1440[17] Zhang Y, Soh Y C, Chen W. Robust information filter for decentralized estimation. Automatica, 2005, 41(12): 2141-2146[18] Nelson T R, Freeman R A. Decentralized H∞ filtering in a multi-agent system. In: Proceedings of the American Control Conference. St. Louis, USA: IEEE, 2009. 5755-5760[19] Olfati-Saber R, Shamma J S. Consensus filters for sensor networks and distributed sensor fusion. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain: IEEE, 2005. 6698-6703[20] Freeman R A, Yang P, Lynch K M. Stability and convergence properties of dynamic average consensus estimators. In: Proceedings of the 45th IEEE Conference on Decision and Control. San Diego, USA: IEEE, 2006. 338-343[21] Zhu M, Martinez S. Discrete-time dynamic average consensus. Automatica, 2010, 46(2): 322-329[22] Freeman R A, Nelson T R, Lynch K M. A complete characterization of a class of robust linear average consensus protocols. In: Proceedings of the American Control Conference. Baltimore, USA: IEEE, 2010. 3198-3203[23] Simon D. Optimal State Estimation: Kalman, H_∈fty and Nonlinear Approaches. New Jersey: John Wiley and Sons, 2006. 333-372[24] Olfati-Saber R, Fax J A, Murray R M. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 2007, 95(1): 215-233[25] Lewis F L, Xie L, Popa D. Optimal and Robust Estimation: with an Introduction to Stochastic Control Theory (Second edition). Boca Raton: CRC Press, 2008. 387-405[26] Shen B, Wang Z D, Hung Y S. Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case. Automatica, 2010, 46(10): 1682-1688[27] Ugrinovskii V A. Distributed robust filtering with H∞ consensus of estimates. In: Proceedings of the American Control Conference. Baltimore, USA: IEEE, 2010. 1374-1379[28] 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

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