基于不确定模型误差系统的变分贝叶斯STCKF  

Variational Bayesian STCKF for systems with uncertain model errors

作　　者：马天力[1] 王新民[1] 彭程[1] 李婷[1] 边琦[1] 

机构地区：[1]西北工业大学自动化学院,西安710129

出　　处：《控制与决策》2016年第12期2255-2260,共6页Control and Decision

基　　金：陕西省自然科学基金项目(2014JQ8342);总装备部基金项目(91xxxxxx43)

摘　　要：强跟踪容积卡尔曼滤波器在对含有模型误差和时变噪声的非线性系统进行滤波时,容易出现性能降低甚至发散.鉴于此,提出一种基于变分贝叶斯的强跟踪容积卡尔曼滤波算法.该算法运用虚拟噪声法补偿模型误差,假设虚拟噪声均值非零,且满足高斯分布,虚拟噪声方差服从逆gamma分布,在强跟踪容积卡尔曼滤波器估计状态的同时,采用变分贝叶斯推理估计虚拟噪声参数.仿真结果表明,所提出算法对含模型误差与时变噪声的非线性系统具有较好的估计精度,相比于自适应算法具有更强的鲁棒性.A strong tracking cubature Kalman filter based on variational Bayesian inference is proposed in order to handle the problem of the nonlinear system with model errors and time-varying noise. By using the fictitious noise compensating technique, the model errors are compensated. The fictitious noise is built by assuming the non-zero mean of noise is Gaussian,and its covariance belongs to the inverse Gamma distribution. The parameter of fictitious noise is estimated by the variational Bayesian inference and the state is estimated by using the strong tracking cubature Kalman filter. The simulation results show that the proposed algorithm can achieve better accuracy and robustness than the adaptive algorithm for the nonlinear system with model errors and time-varying noise.

关 键 词：强跟踪滤波器 容积卡尔曼滤波器 模型误差 时变噪声 虚拟噪声法 变分贝叶斯理论 

分 类 号：V241.62[航空宇航科学与技术—飞行器设计]