Multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter
Multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter
复制标题
基于多衰落因子的强跟踪变分贝叶斯自适应卡尔曼滤波器
DOI：
10.1016/j.measurement.2021.109139
复制
发表时间：
2021-02-24
期刊：
影响因子：
5.6
通讯作者：
Li, Fangchao
中科院分区：
文献类型：
--
作者：
Pan, Cheng;Gao, Jingxiang;Li, Fangchao
如果系统模型或噪声的统计特性不准确，过去的测量值将直接影响当前状态估计的准确性，甚至导致滤波发散。为了克服上述困难，提出了一种基于多衰落因子的强跟踪变分贝叶斯自适应卡尔曼滤波器。首先，采用逆Wishart分布对测量噪声协方差矩阵进行建模。然后，利用指数加权法估计的新息协方差矩阵和修正后的测量噪声协方差矩阵构造标量衰落因子。接着，计算多个衰落因子以校正预测误差协方差矩阵。最后，利用变分贝叶斯方法得到了观测噪声协方差阵和状态的局部最优估计。目标跟踪仿真结果表明，与现有滤波器相比，该算法对预测误差协方差矩阵和测量噪声协方差矩阵具有更好的跟踪能力。
If the system model or the statistical characteristics of noise are inaccurate, the past measurements will directly affect the accuracy of current state estimation or even lead to filtering divergence. To overcome above difficulties, a multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed. Firstly, the inverse Wishart distribution is adopted to model the measurement noise covariance matrix. Secondly, the remodified measurement noise covariance matrix and the innovation covariance matrix estimated by exponential weighting method are employed to construct the scalar fading factor. Next, the multiple fading factors are calculated to correct the predicted error covariance matrix. Finally, the local optimal estimations of measurement noise covariance matrix and state are obtained by variational Bayesian approach. The target tracking simulations verify that the proposed algorithm has better tracking ability for the predicted error covariance matrix and the measurement noise covariance matrix compared with the existing filters.