| 王东宇,张慧君,李孝辉,和 涛.遮挡环境下的基于 AKF 组合导航定位方法研究[J].电子测量与仪器学报,2023,37(5):171-179 |
| 遮挡环境下的基于 AKF 组合导航定位方法研究 |
| Research on positioning in covering environment with an AKF-based integrated navigation system |
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| DOI： |
| 中文关键词: 遮挡环境 GNSS / INS 紧组合导航 自适应卡尔曼滤波 |
| 英文关键词:covering environment GNSS / INS tightly coupled navigation adaptive Kalman filte |
| 基金项目:中国科学院西部青年学者(XAB2021YN24,XAB2021YN28)项目资助 |
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| Author | Institution |
| Wang Dongyu | 1. National Time Service Center, Chinese Academy of Sciences,2. Key Laboratory of Precision Navigation and Timing Technology, National Time Service Center,Chinese Academy of Sciences,3. University of Chinese Academy of Sciences |
| Zhang Huijun | 1. National Time Service Center, Chinese Academy of Sciences,2. Key Laboratory of Precision Navigation and Timing Technology, National Time Service Center,Chinese Academy of Sciences,3. University of Chinese Academy of Sciences |
| Li Xiaohui | 1. National Time Service Center, Chinese Academy of Sciences,2. Key Laboratory of Precision Navigation and Timing Technology, National Time Service Center,Chinese Academy of Sciences,3. University of Chinese Academy of Sciences |
| He Tao | 1. National Time Service Center, Chinese Academy of Sciences,2. Key Laboratory of Precision Navigation and Timing Technology, National Time Service Center,Chinese Academy of Sciences,3. University of Chinese Academy of Sciences，4. Kunming Shipborne Equipment Research and Test Center, China State Shipbuilding Corporation Limited |
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| 全文下载次数: 468 |
| 中文摘要: |
| GNSS / INS 组合导航系统已经得到广泛应用。 但是在 GNSS 系统受到环境限制时,松组合方案会失效。 星座几何布局
频繁变化等原因,会造成紧组合导航定位精度大幅下降。 针对这一问题,本文采用基于新息的自适应卡尔曼滤波紧组合导航方
法,设计了城市环境车载实验方案。 该方法以伪距、伪距率的误差作为观测量,进行滤波估算,同时根据新息的方差理论值与实
际值,构建自适应因子,对系统的量测噪声阵进行调整,进一步修正系统误差。 通过实验验证,在可视卫星数低于 4 颗的时间
内,此方法的三维定位误差相较于使用传统的卡尔曼滤波方法定位误差降低了约 24. 2%;在卫星可视条件频繁变化环境中,定
位误差最大降低了约 60%。 结果表明此方法在观测条件较差时,可以对卫星伪距的观测噪声进行调整,降低了状态误差,增强
滤波器的鲁棒性,从而提供更精准的位置信息。 |
| 英文摘要: |
| GNSS / INS integrated navigation system has been widely used. However, the loosely coupled system will degrade when the
GNSS system is subject to environmental constraints. Meanwhile, the tightly coupled system of GNSS / INS will significantly decrease the
positioning accuracy due to the frequent changes in constellation geometry layout. To solve this problem, the adaptive Kalman filter tight
integrated navigation method based on new information is used in this paper to design a vehicle-mounted experiment scheme in an urban
environment. It takes the errors of the pseudo-range and pseudo-range rate as the observation quantity to perform filtering estimation. At
the same time, the adaptive factor is determined according to the innovation variance’s theoretical and actual values, and the system’s
measurement noise is adjusted to correct the system errors further. Experimental results show that when the number of visible satellites is
less than four, the 3D positioning error of this method is reduced by about 24. 2% compared with the traditional Kalman filtering method.
In the environment of frequent changes in visible satellite conditions, the maximum positioning error is reduced by about 60%. The
results showed that this method could adjust the observation noise of satellite pseudo-distance when the observation conditions are poor,
reduce the state error, enhance the robustness of the filter, and provide more accurate position information. |
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