基于学习的核偏最小二乘法优化扩展卡尔曼滤波  

Optimized Extended Kalman Filter Based on Learning Kernel Partial Least Squares

作　　者：白晓波[1] 邵景峰[1] 和征[1] 田建刚 BAI Xiao-bo;SHAO Jing-feng;HE Zheng;TIAN Jian-gang(School of Management,Xi＇ an Polytechnic University,Xi＇ an 710048,China;Department of Information and Arms,Army Academy of Border and Coastal Defenee,Xi＇ an 710108,China)

机构地区：[1]西安工程大学管理学院,陕西西安710048 [2]陆军边海防学院信息与兵种教研室,陕西西安710108

出　　处：《计算机与现代化》2018年第9期110-117,共8页Computer and Modernization

基　　金：国家科技支撑计划项目(2014BAF07B01);陕西省工业科技攻关项目(2017GY-039);陕西省教育厅服务地方专项计划项目(16JF009);2017年中国纺织工业联合会科技指导性计划项目(2017067);2017年"纺织之光"中国纺织工业联合会高等教育教学改革项目(2017BKJGLX160)

摘　　要：为了解决非线性系统参数不确定和噪声信息不准确导致EKF随时序估计误差较大的问题,利用核偏最小二乘法与系统方程参数和噪声信息无关的特点优化EKF。先将量测数据和EKF的收敛估计作为学习样本,建立KPLS预测模型,然后,融合KPLS和EKF的预测值进行状态估计;同时,若状态估计的收敛判据为真,将估计值作为学习样本,并利用滑动窗口更新KPLS核矩阵,使KPLS能时序预测;若收敛判据为假,则更新量测协方差。最后,通过实验仿真的方法,分析KPLS-EKF算法的收敛性和性能。实验结果表明:KPLS-EKF能够有效地解决非线性系统参数和噪声信息不准确导致的EKF误差较大的问题。To solve the problem that the error of EKF estimation is larger with time sequence caused by uncertain parameters and inaccuracy noise information of none-liner system,the character of kernel partial least square which is independent of system equation parameters and noise information is used to optimize EKF. Firstly,measurement and convergence estimation value are as studying samples to build prediction model. And then,prediction values of KPLS and EKF are fused to estimate system status.Mean while,if convergence criterion of status estimation is true,then estimation values are as studying samples,and the sliding window is used to update kernel matrix,which makes KPLS have the ability to be predicted with time sequence. Else if convergence criterion of status estimation is false,then the measurement covariance will be updated. Finally,convergence and performance of KPLS-EKF are analyzed. The experimental results show that the proposed problem can be effectively solved by KPLSEKF.

关 键 词：非线性 核偏最小二乘法 扩展卡尔曼滤波 收敛判据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]