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| 一种新的车载DR系统自适应卡尔曼滤波模型 | |
| 引用本文： | 房建成.一种新的车载DR系统自适应卡尔曼滤波模型[J].北京航空航天大学学报,1998,24(6):722-725. |
| 作者姓名： | 房建成 |
| 作者单位： | 1. 北京航空航天大学 宇航学院; 2. 东南大学 |
| 摘 要： | 提出了车载DR系统(Dead-Reckoning System)改进的自适应扩展卡尔曼滤波模型及其滤波算法.由于考虑了速率陀螺漂移误差中的马尔柯夫过程成分,和采用描述机动载体运动的"当前"统计模型及自适应算法,提高了DR系统模型的准确性.计算机仿真结果表明,应用该模型和算法与改进前相比,DR系统的定位精度得到明显提高. |
| 关 键 词： | 陆地导航 航迹推算法 卡尔曼滤波 滤波算法 |
| 收稿时间： | 1998-06-09 |
New Modified Adaptive Extended Kalman Filterof DR System for Land Vehicle Navigation |
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| Fang Jiancheng,Shen Gongxun,Wan Dejun.New Modified Adaptive Extended Kalman Filterof DR System for Land Vehicle Navigation[J].Journal of Beijing University of Aeronautics and Astronautics,1998,24(6):722-725. | |
| Authors: | Fang Jiancheng Shen Gongxun Wan Dejun |
| Institution: | 1. Beijing University of Aeronautics and Astronautics,School of Astronautics; 2. Southeast University |
| Abstract: | A new observation equation of the dead reckoning (DR) system for land vehicle navigation is presented, and a modified adaptive extended Kalman filter and its filtering algorithm for the DR system are proposed. By consideration of Markov process component in the random drift of rate gyros, and application of a current statistical model and adaptive algorithm for estimating maneuvering vehicles, the accuracy of the DR system model is greatly improved.Furthermore, in order to enhance the robustness and dynamatic performance of the extended kalman filter, a new adaptive filtering algorithm is researched.Computer simulation results show that the positioning accuracyand possiblility of the DR system can be greatly improved by this filter and adaptive algorithm. |
| Keywords: | navigation dead reckoning kalman filtering filering algorithm |
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