引用本文:刘永桂,胥布工,史步海.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)[J].控制理论与应用,2013,30(7):898~908.[点击复制]
LIU Yong-gui,XU Bu-gong,SHI Bu-hai.Kalman filtering for stochastic systems with consecutive packet losses and measurement time delays[J].Control Theory and Technology,2013,30(7):898~908.[点击复制]
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)
Kalman filtering for stochastic systems with consecutive packet losses and measurement time delays
摘要点击 3857  全文点击 1960  投稿时间：2012-03-13  修订日期：2013-04-19
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DOI编号  10.7641/CTA.2013.12052
  2013,30(7):898-908
中文关键词  滤波器设计  连续丢包  无线传感器网络  测量时延
英文关键词  filter design  consecutive packet losses  wireless sensor networks  measurement time delay
基金项目  This work was 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), and the Key Laboratory of Systems and Network Control, Ministry of Education.
作者单位E-mail
刘永桂 华南理工大学 自动化科学与工程学院 auygliu@scut.edu.cn 
胥布工 华南理工大学 自动化科学与工程学院  
史步海* 华南理工大学 自动化科学与工程学院  
中文摘要
      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.
英文摘要
      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.