顾及有色噪声的卡尔曼滤波在多路径误差削弱中的应用

李川; 魏世玉; 刘星; 刘恋; 胡祝敏

doi:DOI:10.13442/j.gnss.1008-9268.2019.01.009

顾及有色噪声的卡尔曼滤波在多路径误差削弱中的应用

doi: DOI:10.13442/j.gnss.1008-9268.2019.01.009

1.
重庆市地质灾害自动化监测工程技术研究中心, 重庆 401120
2.
重庆大学土木工程学院, 重庆 400044

详细信息

作者简介:
李川 (1990-),男,工程师,硕士研究生,主要从事GNSS测量数据处理及自动化监测设备研发.

通讯作者:
李川 E-mail:641419444@qq.com

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- 被引次数: 0
出版历程
- 刊出日期: 2019-02-15

The application of Kalman filter with colored noise in mitigation of multi-path error

1.
Chongqing Engineering Research Center of Automatic Monitoring for Geological Hazards,Chongqing 401120, China
2.
College of Civil Engineering of Chongqing University, Chongqing 400044, China

摘要

摘要: 多路径误差为一时空环境效应,难以构建准确数学模型消除其影响,且该误差在基线两端不具有空间相关性,运用现有差分技术也无法很好消除,是高精度短基线测量中主要误差之一．为进一步削弱多路径误差,本文以监测站坐标时间序列中多路径误差为研究对象,根据多路径误差在历元间的时变特性,建立多路径误差状态空间模型,采用标准卡尔曼滤波和顾及有色噪声的卡尔曼滤波从监测站第一天双差固定解坐标残差序列中估计多路径误差改正序列,并根据多路径误差的周日重复特性,利用第一天得到的多路径误差改正序列对之后各天坐标序列进行改正．最后通过实验分析,得出顾及有色噪声的卡尔曼滤波估计方法优于标准卡尔曼滤波的结论．研究方法对提高GNSS定位精度具有重要实用价值．
- 多路径误差 /
- 有色噪声 /
- 卡尔曼滤波 /
- 周日重复性
Abstract: Multi-path error is a space-time environment effect, it is difficult to eliminate it through building accurate mathematical model, and the error also has no spatial correlation at both ends of the baseline, it can't eliminate it through using the differential technology, it is one of the main errors in GNSS high accuracy measurement of short baseline. To further mitigate the multi-path error, this paper take the multi-path error in GNSS carrier phase observation value as the research object, establish the multi-path error state space model, estimate the multipath error correction sequence from coordinate residual sequence adopt the standard Kalman filter and the Kalman filter with colored noise, and based on the daily repetition characteristics of multi-path error, correct the coordinate sequences of the subsequent days using the multi-path error correction sequence obtained on the first day. Finally, through the experimental analysis, it is concluded that the Kalman filter with colored noise estimation method is better than the standard Kalman filter. The research results are of great practical value to improve GNSS positioning accuracy.
- GNSS /
- multi-path error /
- colored noise /
- Kalman filter

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