非线性离散系统的相关观测融合时变Kalman滤波  

Correlated measurement fusion time-vary Kalman filtering algorithms of non-linear discrete system

作　　者：杨红[1] 罗飞[1] 李艳[1] 许玉格[1] 

机构地区：[1]华南理工大学自动化科学与工程学院,广州510641

出　　处：《控制与决策》2010年第5期669-675,共7页Control and Decision

基　　金：国家自然科学基金项目(60774032);教育部高等学校博士学科点专项科研基金项目(20070561006)

摘　　要：针对带相关观测噪声和带不同观测函数的多传感器离散非线性系统,利用推广的离散Kalman滤波方法对状态系统和观测系统进行线性化处理,提出了基于岭估计的加权最小二乘(REWLS)分布式融合Kalman滤波算法.以风险函数为评价指标,利用信息滤波器比较了各种观测融合Kalman滤波算法,其中REWLS分布式融合算法精度最高.同时,分布式融合算法减少了计算负担,便于实时应用.仿真例子表明了理论分析的正确性.For the multi-sensor discrete non-linear systems with correlated measurement noises and different measurement functions,the method of extended discrete Kalman filtering is used to make these systems of state and observation linearized, and ridge estimation weighted least square（REWLS） distributed fusion Kalman filtering is presented.On the basis of risk function,information filtering is utilized to compare these measurement fusion Kalman filtering algorithms,and REWLS distributed fusion algorithm has the highest precision.The distributed fusion algorithms can reduce the computational burden and is suitable for real time application.The simulation examples show the effectiveness of theory analysis.

关 键 词：非线性离散系统 KALMAN滤波 多传感器信息融合 分布式观测融合 集中式观测融合 岭估计 

分 类 号：O211.64[理学—概率论与数理统计]