电子科技大学学报2012，Vol.41Issue(3)：359-363,5.DOI:10.3969/j.issn.1001-0548.2012.03.006
双重迭代变分贝叶斯自适应卡尔曼滤波算法
Dual Recursive Variational Bayesian Adaptive Kalman Filtering Algorithm
摘要
Abstract
A new adaptive Kalman filtering algorithm is presented. The new algorithm assumes that the variance relationship between process noise and measurement noise is known, but both kinds of variance are unknown and varying with time. At first, let the process noise variance at the current time point be equal to that at the prior time point. Applying the method of variational Bayesian approximation, the measurement noise variance and state estimation are solved under the framework of Kalman filter, and then a new process noise variance is obtained via the function relationship. After the process above is implemented for some runs, the final state estimation and covariance are obtained. Experimental results show that the new algorithm has higher accuracy; Furthermore, the new algorithm still has strong robustness when the assumption is uncertain.关键词
自适应卡尔曼滤波/噪声方差未知/状态估计/变分贝叶斯近似Key words
adaptive Kalman filter/ noise variance unknown/ state estimation/ variational Bayesian approximation分类
信息技术与安全科学引用本文复制引用
陈金广,李洁,高新波..双重迭代变分贝叶斯自适应卡尔曼滤波算法[J].电子科技大学学报,2012,41(3):359-363,5.基金项目
国家自然科学基金(60832005,61125204) （60832005,61125204）
