摘要
针对线性高斯状态空间模型中的噪声统计特性时变时,变分贝叶斯自适应卡尔曼滤波效果会受影响的问题,提出了基于强跟踪原理的改进算法。该算法选择测量噪声模型为逆Wishart分布,将系统状态与时变的测量噪声协方差作为待估参数,利用变分贝叶斯方法对二者迭代递推估计。测量噪声协方差的最优估计结果再作为时变参数引入到基于强跟踪原理的次优渐消因子中,以提高其对状态预测协方差的修正精度。仿真结果表明,改进算法在噪声时变的线性高斯系统中能自适应地跟踪测量噪声协方差,有效克服过程噪声协方差时变的影响,估计结果的收敛速度和精度有很大改善。
Aiming at the problem that the effect of variational Bayesian adaptive Kalman filtering will be affected when the noise statistical characteristics in the linear Gaussian state space model are time-varying an improved algorithm based on the principle of strong tracking is proposed.The measurement noise model is selected as the inverse Wishart distribution the system state and time-varying measurement noise covariance are taken as the parameters to be estimated and variational Bayesian approach is used for their recursive estimation.The optimum estimation result of the measurement noise covariance is then taken as a time-varying parameter and introduced into the sub-optimal fading factor based on the strong tracking principle to improve the correction accuracy of the covariance of state prediction.Simulation results show that:1)The improved algorithm can track the measurement noise covariance adaptively in the linear Gaussian system with time-varying noise and effectively overcome the influence of time-varying noise covariance;and 2)The convergence speed and accuracy of the estimated results are greatly improved.
作者
谈发明
赵俊杰
TAN Faming;ZHAO Junjie(Jiangsu University of Technology,Information Center,Changzhou 213001 China;Jiangsu University of Technology,School of Electrical and Information Engineering,Changzhou 213001 China)
出处
《电光与控制》
CSCD
北大核心
2020年第1期12-16,36,共6页
Electronics Optics & Control
基金
国家自然科学基金青年科学基金(61803186)
关键词
强跟踪
变分贝叶斯
噪声
次优渐消因子
精度
strong tracking
variational Bayesian
noise
sub-optimal fading factor
precision
