一种基于冗余测量的自适应卡尔曼滤波算法
收稿日期: 2014-06-27
修回日期: 2015-01-14
网络出版日期: 2015-01-19
基金资助
卫星应用研究院创新基金项目(2014_CXJJ-DH_06)
A redundant measurement adaptive Kalman filter algorithm
Received date: 2014-06-27
Revised date: 2015-01-14
Online published: 2015-01-19
Supported by
Open Research Fund of The Academy of Satellite Application (2014_CXJJ-DH_06)
周启帆 , 张海 , 王嫣然 . 一种基于冗余测量的自适应卡尔曼滤波算法[J]. 航空学报, 2015 , 36(5) : 1596 -1605 . DOI: 10.7527/S1000-6893.2015.0001
In order to solve the problems of current adaptive Kalman filter, this paper proposes a redundant measurement adaptive Kalman filter (RMAKF) in the situation that the measurement noise variance is unknown. This method could accurately estimate the statistical characteristics of measurement noise through calculating the first and second order difference sequences and adaptively tuning the variance matrix of measurement noise R in the process to improve the accuracy and precision. The simulation results show that when the algorithm is applied in GPS/INS loosely coupled integrated system, the proposed method is capable of estimating the noise variance when the statistical characteristic is unknown or changed. The simulation also shows that the filtering results has a great improvement compared with other adaptive Kalman filter when using low-accuracy inertial sensors.
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