期刊文献+

随机系统中能容忍连续丢包和测量时延的卡尔曼滤波(英文) 被引量:6

Kalman filtering for stochastic systems with consecutive packet losses and measurement time delays
在线阅读 下载PDF
导出
摘要 通过转换原线性系统到能容忍连续丢包和测量时延的随机参数系统,推导了一个最优线性滤波器.给出一个仿真例子,比较已存在的结果,仿真结果表明被提出的线性滤波器有优越的性能.然而,该滤波器不能应用于非线性系统.从应用角度,为非线性系统提出了一个增强型的滤波器.而且,该增强型的滤波器能成功地应用于不可靠的无线传感器网络场景来跟踪移动目标.这些滤波器只依靠测量值的达到概率,而不需要知道某一时刻测量是否接收.仿真说明了被提出的增强型滤波器不仅能改善实时目标跟踪的鲁棒性,而且比标准的扩展卡尔曼滤波器能够提供更精确的估计. An optimal linear filter is derived through transferring the original linear systems to stochastic parameter systems with consecutive packet losses and time delays.A numerical simulation example is performed with results showing that this linear filter has superior performance to other existing approaches.However,the proposed filter cannot be applied to nonlinear systems.From the practical perspective,an enhanced filter is proposed and is extended to nonlinear systems.This enhanced filter has been applied successfully to an unreliable wireless sensor network(WSNs) scenario to track a moving target.The proposed filters depend only on the measurement arrival probability at all time but do not require knowing whether a measurement is received at a specific time instant.Simulations show that the proposed enhanced filter not only improves the robustness for real-time target tracking in WSNs,but also provides more accurate estimations than the standard extended Kalman filter.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2013年第7期898-908,共11页 Control Theory & Applications
基金 supported by the National Natural Science Foundation of China(Nos.61174060,61174070) the Specialized Research Fund for the Doctoral Program(Nos.20120172120034,20110172110033) the Fundamental Research Funds for the Central Universities(No.2012ZM0101) the Guangdong Provincial Office of Science and Technology Research Projects(No.2011B090400507) the Dongguan Science and Technology Plan Project(No.2012108102005) the Key Laboratory of Systems and Network Control,Ministry of Education
关键词 滤波器设计 连续丢包 无线传感器网络 测量时延 filter design consecutive packet losses wireless sensor networks measurement time delays
  • 相关文献

参考文献25

  • 1AKYILDIZ I, SU W, SANKARASUBRAMANIAM Y, et al. Wireless sensor networks: a survey [J], Computer Networks, 2002, 38(4): 393-442,.
  • 2NAHI N, Optimal recursive estimation with uncertain observation [J]. IEEE Transactions on Information Theory, 1969, 15(4): 457 - 462.
  • 3HADIDA M, SCHAWARTZ S. Linear recursive state estimators under uncertain observations [J]. IEEE Transactions on Information Theory, 1979,24(6): 944 - 948.
  • 4COSTA O. Stationary filter for linear minimum mean square error estimator of discrete-time Markovian jump systems [J]. IEEE Transactions on Automatic Control, 2002, 47(8): 1351-1356.
  • 5SMITH S, SEILER P. Estimation with lossy measurements: Jump estimators for jump systems [J]. IEEE Transactions on Automatic Control, 2003, 48(12): 2163 - 2171.
  • 6NILSSON J, BERNHARDSSON B, WITTENMARK B. Stochastic analysis and control of real-time systems with random time delays [J]. Automatica, 1998,34(1): 57 - 64.
  • 7LING Q, LEMMON M. Real-time scheduling of networked control systems with dropouts governed by a Markov chain [C]//American Control Conference. Denver: [s.n.], 2003: 4845 - 4550.
  • 8SINOPOLI B, SCHENATO L, FRANCESCHETTI M, et al. Kalman filtering with intermittent observations [J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1453 - 1464.
  • 9SCHENATO L. Optimal estimation in networked control systems subject to random delay and packet drop [J]. IEEE Transactions on Automatic Control, 2008, 53(5): 1311 - 1317.
  • 10MOSTOFI Y, MURRAY R. To drop or not to drop: Design principles for Kalman filtering over wireless fading channels [J]. IEEE Transactions on Automatic Control, 2009, 54(2): 376 - 381.

二级参考文献10

  • 1W.Xiao,J.Wu,L.Shue,et al.A prototype ultrasonic sensor network for tracking of moving targets[].The st IEEE Conference on Industrial Electronics and Applications.2006
  • 2Y.Toh,W.Xiao,L.Xie.A wireless sensor network target tracking system with distributed competition based sensor scheduling[].Proceedings of the rd International Conference on Intelligent SensorsSensor Networks and Information.2007
  • 3H.Wang,G.Pottie,K.Yao,et al.Entropy-based sensor selectionheuristic for target localization[].The rd International Symposium on Information Processing in Sensor Networks.2004
  • 4E.Daeipour,Y.Bar-Shalom,X.Li.Adaptive beam pointing control of a phased array radar using an IMM estimator[].Proceedings of the American Control Conference.1994
  • 5M.Bhardwaj,A.P.Chandrakasan.Bounding the lifetime of sensor networks via optimal role assignments[].Proceedings of IEEE Information Communications Conference (INFOCOM ).2002
  • 6X.Sheng,Y.Hu.Energy based acoustic source localization[].Proceedings of Information Processing in Sensor Networks.2003
  • 7Zhao F,Liu J,Liu J,et al.Collaborative signal and information processing: An information directed approach[].Proceedings of Tricomm.2003
  • 8Bar Shalom Y,Li X R,Kirubarajan T.Estimationwith applications to tracking and navigation:theory,algorithms and software[]..2001
  • 9Kirubarajan T,Bar-Shalom Y,Blair W D,et al.IMMPDAF for Radar Management and Tracking Benchmark with ECM[].IEEE Transactions on Aerospace and Electronic Systems.1998
  • 10M.Kalandros,,L.Y.Pao.Covariance Control for Multisensor Systems[].IEEE Transactions on Aerospace and Electronic Systems.2002

共引文献8

同被引文献18

引证文献6

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部