Combined adaptive robust Kalman filter algorithm
Combined adaptive robust Kalman filter algorithm
复制标题
组合自适应鲁棒卡尔曼滤波算法
DOI：
10.1088/1361-6501/abf57c
复制
发表时间：
2021-04
影响因子：
2.4
通讯作者：
He Zhijie
中科院分区：
文献类型：
--
作者：
Lin Xu;Li Wei;Li Shaoda;Ye Jiang;Yao Chaolong;He Zhijie
车辆、行人等动、静态物体的精确定位是一项关键技术。全球导航卫星系统信号是实现精确定位所需的主要信号源，并且在精确定位中使用的最佳估计方法是卡尔曼滤波（KF）。标准KF只能在数学模型已经确定、噪声特性已知的条件下才能达到最优估计结果。但当存在测量离群值和先验噪声信息偏差时，KF的准确性就无法保证。为有效解决上述问题，提出了一种组合自适应鲁棒卡尔曼滤波（CARKF）算法.首先，利用鲁棒卡尔曼滤波方法抑制观测野值对卡尔曼滤波精度的影响。然后，利用基于IGGIII方法的相关观测值稳健估计器抵抗异常新息对其自相关序列和互相关序列的影响，保证滤波后新息统计量的相关函数序列的正确估计.最后，利用自协方差最小二乘法消除噪声协方差之间的耦合影响，准确估计未知噪声协方差信息。此外，采用迭代策略消除先验噪声协方差偏差引起的误差的影响。3个实验结果表明，CARKF方法不仅能有效抵抗观测野值对滤波精度和噪声协方差估值的影响，而且能克服噪声先验偏差带来的误差影响，同时准确估计两类未知噪声协方差信息。
The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.
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DOI：
10.1109/9780470544334.ch9
发表时间：
2001
期刊：
Comput. Electron. Agric.
影响因子：
--
作者：
T. Başar
通讯作者：
T. Başar
影响因子：
4.9
作者：
Yunzhong Shen;Zebo Zhou;Bofeng Li
通讯作者：
Bofeng Li
DOI：
10.1109/tie.2019.2901663
发表时间：
2020-03
期刊：
IEEE transactions on industrial electronics (1982)
影响因子：
--
作者：
Chen Y;Li H;Qiu Z;Wang TD;Oldham KR
通讯作者：
Oldham KR
影响因子：
4.4
作者：
Y. Yang;M. Cheng;C. Shum;B. Tapley
通讯作者：
Y. Yang;M. Cheng;C. Shum;B. Tapley
基于马氏距离作为异常值判断标准的鲁棒卡尔曼滤波
影响因子：
4.4
作者：
Chang, Guobin
通讯作者：
Chang, Guobin