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. doi: 10.3969/j.issn.1001-0548.2012.03.006 引用本文: 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. doi: 10.3969/j.issn.1001-0548.2012.03.006 CHEN Jin-guang, LI Jie, GAO Xin-bo. Dual Recursive Variational Bayesian Adaptive Kalman Filtering Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 359-363. doi: 10.3969/j.issn.1001-0548.2012.03.006 Citation: CHEN Jin-guang, LI Jie, GAO Xin-bo. Dual Recursive Variational Bayesian Adaptive Kalman Filtering Algorithm[J]. Journal of University of Electronic Science and Technology of China , 2012, 41(3): 359-363. doi: 10.3969/j.issn.1001-0548.2012.03.006 提出了一种新的自适应卡尔曼滤波算法。该算法假设系统过程噪声方差和量测噪声方差之间存在的函数关系已知,两种噪声方差随着时间变化且均未知。先令当前时刻的过程噪声方差等于前一时刻的过程噪声方差,通过变分贝叶斯近似的方法,在卡尔曼滤波框架下迭代求解当前时刻的量测噪声方差和状态估计,再利用假设中的函数关系获得新的过程噪声方差。对上述过程多次迭代,最终获得状态估计及协方差。仿真实验结果表明,该算法具有较高的滤波精度;在假设条件不确知的情况下仍具有较强的鲁棒性。 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 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. Key words: adaptive Kalman filter /  noise variance unknown /  state estimation /  variational Bayesian approximation Keywords:

adaptive Kalman filter /

noise variance unknown /

state estimation /

variational Bayesian approximation

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.

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. doi: 10.3969/j.issn.1001-0548.2012.03.006 引用本文: 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. doi: 10.3969/j.issn.1001-0548.2012.03.006 CHEN Jin-guang, LI Jie, GAO Xin-bo. Dual Recursive Variational Bayesian Adaptive Kalman Filtering Algorithm[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 359-363. doi: 10.3969/j.issn.1001-0548.2012.03.006 Citation: CHEN Jin-guang, LI Jie, GAO Xin-bo. Dual Recursive Variational Bayesian Adaptive Kalman Filtering Algorithm[J]. Journal of University of Electronic Science and Technology of China , 2012, 41(3): 359-363. doi: 10.3969/j.issn.1001-0548.2012.03.006

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