基于STF和7thCQKF的状态突变系统跟踪算法  

Tracking algorithm for system with abrupt state change based on STF and 7thCQKF

作　　者：施丽红[1] 孙海燕[2] SHI Lihong;SUN Haiyan(Electronic Commerce and Logisitics Department,Jiangsu Vocational College of Business,Nantong Jiangsu 226011,China;Jiangsu Key Laboratory of ASIC Design,Nantong University,Nantong Jiangsu 226019,China)

机构地区：[1]江苏商贸职业学院电商与物流学院,江苏南通226011 [2]南通大学江苏省专用集成电路设计重点实验室,江苏南通226019

出　　处：《太赫兹科学与电子信息学报》2021年第3期419-425,共7页Journal of Terahertz Science and Electronic Information Technology

基　　金：江苏省第五期“333工程”科研资助计划资助项目(BRA2018220)。

摘　　要：针对传统非线性滤波算法对状态突变的鲁棒性较差,存在跟踪缓慢甚至失效的问题,提出了强跟踪七阶正交容积卡尔曼滤波(ST-7thCQKF)算法。算法将对非线性系统滤波效果良好的七阶正交容积卡尔曼滤波(7thCQKF)与强跟踪滤波(STF)融合,通过在7thCQKF的预测协方差中引入渐消因子调节增益矩阵,提高算法对状态突变系统的跟踪效果。仿真和实验结果表明,ST-7thCQKF能够较好地处理状态突变系统的跟踪问题。Aiming to the problem that traditional nonlinear filters have bad robustness for abrupt state change system,the tracking is slow and even becomes invalid,Strong Tracking seventh-degree Cubature Quadrature Kalman Filter(ST-7thCQKF)is proposed.The algorithms fuses seventh-degree Cubature Quadrature Kalman Filter(7thCQKF),which shows good filtering effect for nonlinear system,with Strong Tracking Filter(STF),the tracking precision of abrupt state change system is improved through introducing fading factor into the prediction error covariance of 7thCQKF to adjust the gain matrix in filter process.The simulation and experiment results show that,ST-7thCQKF can well deal with the problem of state tracking for system with abrupt state.

关 键 词：状态突变 七阶正交容积卡尔曼滤波 强跟踪滤波 强跟踪容积卡尔曼滤波 

分 类 号：TN820.4[电子电信—信息与通信工程] TP953[自动化与计算机技术]