航空矢量重力测量中光纤陀螺随机漂移误差实时补偿方法

doi:10.11947/j.AGCS.2017.20160174

测绘学报 ›› 2017, Vol. 46 ›› Issue (2): 144-150.doi: 10.11947/j.AGCS.2017.20160174

航空矢量重力测量中光纤陀螺随机漂移误差实时补偿方法

王峥¹, 李建成^1,2

1. 武汉大学测绘学院, 湖北武汉 430079;
2. 武汉大学地球空间环境与大地测量教育部重点实验室, 湖北武汉 430079

收稿日期:2016-04-18 修回日期:2016-12-16 出版日期:2017-02-20 发布日期:2017-03-07
作者简介:王峥(1986-),女,博士生,研究方向为卫星大地测量、航空重力测量。E-mail:zhengwang@whu.edu.cn
基金资助:
国家自然科学基金（41210006；41504016）；国家973计划（2013CB733301）；地球空间环境与大地测量教育部重点实验室测绘基础研究基金（14-02-01）

Research on the Real-time Compensation of the Fiber Optic Gyroscope Random Drift in Airborne Vector Gravimetry

WANG Zheng¹, LI Jiancheng^1,2

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan 430079, China

Received:2016-04-18 Revised:2016-12-16 Online:2017-02-20 Published:2017-03-07
Supported by:
The National Natural Science Foundation of China (Nos. 41210006,41504016),The National Basic Research Program of China (973 Program) (No. 2013CB733301),Basic Research Foundation Program of Surveying by Key Laboratory of Geospace Environment and Geodesy, Ministry of Education (No. 14-02-01)

摘要/Abstract

摘要： 光纤陀螺随机漂移误差是影响航空矢量重力测量系统姿态解算精度的关键因素。建立模型并在输出中对其补偿是抑制该项误差的有效方法。针对传统ARMA模型只能对平稳随机漂移误差建模，且模型无法满足实时滤波需求的问题，本文引入适用于非平稳随机漂移误差的ARIMA模型，同时给出详细的建模过程，并提出采用实时平均算法消除原始采样序列中常值分量的思路，实现了随机漂移误差的实时Kalman滤波估计。基于本文所提出的模型和实时滤波算法，对光纤陀螺实测数据进行分析，结果表明处理后信号中随机漂移误差的方差减小了46.5%。Allan方差分析结果表明，滤波后角度随机游走系数和角速率随机游走系数分别降低了约50%和40%。本文的结果说明ARIMA模型能够准确描述陀螺的非平稳随机漂移误差。基于实时平均算法的Kalman滤波可实现随机漂移误差的在线估计，有望提高航空矢量重力测量系统的姿态解算精度。

关键词: 光纤陀螺, ARIMA模型, Kalman滤波, Allan方差

Abstract: Random drift error of fiber optic gyroscope is the crucial factor that influences the calculation accuracy of the attitude of airborne vector gravimetry. Modeling and compensating it can restrain this type of error significantly. Given the problem that traditional ARMA model can be only applied in the case of stable random drift, which cannot meet the need of real-time filtering, an ARIMA model (autoregressive integrated moving average) which is suitable for non-stable random drift is introduced along with the detailed procedure in this paper. The algorithm that can eliminate the constant component of original sampling sequence with real-time averaging method is also proposed as well as the real-time Kalman filtering estimation of the random drift. With the methods proposed above, the variance of random drift can be reduced by 46.5%. The analysis of Allan variance suggests that the coefficients of random drift for angle and angular speed have decreased about 50% and 40%, respectively. The results showed that non-stable random drift can be accurately characterized by ARIMA model and that online estimation of random drift can be realized by real-time average algorithm, indicating the potential to improve the calculation accuracy of the attitude of airborne vector gravimetry.

Key words: fiber optic gyroscope (FOG), ARIMA model, Kalman filter, Allan variance

中图分类号:

P224

王峥, 李建成. 航空矢量重力测量中光纤陀螺随机漂移误差实时补偿方法[J]. 测绘学报, 2017, 46(2): 144-150.

WANG Zheng, LI Jiancheng. Research on the Real-time Compensation of the Fiber Optic Gyroscope Random Drift in Airborne Vector Gravimetry[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(2): 144-150.

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航空矢量重力测量中光纤陀螺随机漂移误差实时补偿方法

Research on the Real-time Compensation of the Fiber Optic Gyroscope Random Drift in Airborne Vector Gravimetry

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