引用本文:郝钢,叶秀芬,陈亭.加权观测融合非线性无迹卡尔曼滤波算法[J].控制理论与应用,2011,28(6):753~758.[点击复制]
HAO Gang,YE Xiu-fen,CHEN Ting.Weighted measurement fusion algorithm for nonlinear unscented Kalman filter[J].Control Theory and Technology,2011,28(6):753~758.[点击复制]
加权观测融合非线性无迹卡尔曼滤波算法
Weighted measurement fusion algorithm for nonlinear unscented Kalman filter
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DOI编号  10.7641/j.issn.1000-8152.2011.6.CCTA100425
  2011,28(6):753-758
中文关键词  非线性滤波  无迹卡尔曼滤波器  加权观测融合
英文关键词  nonlinear filtering  unscented Kalman filter  weighted measurement fusion
基金项目  教育部科学技术研究重点资助项目(209038); 黑龙江省自然科学基金资助项目(F201015).
作者单位E-mail
郝钢* 哈尔滨工程大学 自动化学院
黑龙江大学 电子工程学院 		haogang@hlju.edu.cn 
叶秀芬 哈尔滨工程大学 自动化学院  
陈亭 黑龙江大学 电子工程学院  
中文摘要
      针对非线性系统的无迹卡尔曼滤波器(UKF), 应用加权最小二乘(WLS)法, 提出了加权观测融合UKF滤波算法. 证明了加权观测融合UKF滤波算法与集中式观测融合UKF滤波算法在数值上的完全等价性, 因而具有全局最优性. 一个带两传感器非线性系统的仿真例子说明了两种融合算法的有效性及等价性.
英文摘要
      For nonlinear systems, based on the Unscented Kalman filter(UKF), the algorithm of the weighted measurement fusion UKF is presented by using the weighted least squares(WLS) method. It is proved that the weighted measurement fusion UKF is completely numerically identical to the centralized measurement fusion UKF algorithm; and thus, the measurement fusion UKF has global optimality. A simulation example for the nonlinear systems with two sensors shows the effectiveness of the two measurement fusion UKF and verifies the completely numerically equivalence.