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

作　　者：郝钢[1,2] 叶秀芬[1] 陈亭 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001 [2]黑龙江大学电子工程学院,黑龙江哈尔滨150080

出　　处：《控制理论与应用》2011年第6期753-758,共6页Control Theory & Applications

基　　金：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)

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.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.

关 键 词：非线性滤波 无迹卡尔曼滤波器 加权观测融合 

分 类 号：TN713[电子电信—电路与系统]