北京航空航天大学学报 ›› 2015, Vol. 41 ›› Issue (6): 1135-1140.doi: 10.13700/j.bh.1001-5965.2014.0440

• 论文 • 上一篇    下一篇

基于GP+GA的陀螺仪随机误差建模分析

吕琳1,2, 全伟1,2   

  1. 1. 北京航空航天大学 惯性技术重点实验室, 北京 100191;
    2. 北京航空航天大学 新型惯性仪表与导航系统技术国防重点学科实验室, 北京 100191
  • 收稿日期:2014-07-22 出版日期:2015-06-20 发布日期:2015-07-30
  • 通讯作者: 全伟(1977—),男,山东临沂人,副教授,quanwei@buaa.edu.cn,主要研究方向为组合导航、组合定姿. E-mail:quanwei@buaa.edu.cn
  • 作者简介:吕琳(1990—),女,山东聊城人,硕士研究生,lvlinlch1990@163.com
  • 基金资助:
    国家自然科学基金(61374210,61233005,61104198)

Modeling and analysis of gyroscope's random drift based on GP+GA method

LYU Lin1,2, QUAN Wei1,2   

  1. 1. Science and Technology on Initial Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    2. Fundamental Science on Novel Instrument & Navigation System Technology Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2014-07-22 Online:2015-06-20 Published:2015-07-30

摘要: 陀螺仪是惯性导航系统的重要组成部分,其精度依赖于惯性导航系统的精度.为了提高陀螺仪的精度,针对陀螺随机漂移非线性、弱平稳性引起的随机误差,以激光陀螺仪随机漂移时间序列数据为研究对象,首先通过对陀螺仪建模的分析和对激光陀螺仪实时数据的分析和预处理,得到了陀螺漂移误差的离散时间序列;然后对其基于遗传规划(GP)建模,得出了当前时刻陀螺漂移数据和前几时刻的陀螺漂移数据之间的非线性数学模型;最后利用遗传算法(GA)对该模型有数学关系的参数进行优化,得到更高精度的模型.仿真结果表明:与经典自回归(AR)建模优化方法相比,GP+GA建模能够更加有效地反映陀螺仪的随机漂移特性,陀螺仪的方差降低了73.72%,与经典自回归(AR)建模方法相比效果提高了4.72%.该建模方法有效补偿了陀螺仪的随机漂移误差,提高了系统的稳定性.

关键词: 陀螺仪, 遗传规划(GP), 遗传算法(GA), 随机误差建模, 优化

Abstract: Gyroscope is the key component in an inertial navigation system (INS). It depends on the precision of the INS. In order to improve the gyro's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of gyro's random drift, the nonlinear random drift error model based on genetic programming (GP)+genetic algorithm (GA) was established taking the time series of gyro's random drift as study object. Firstly, the time series of gyro's random drift were got through analyzing and preprocessing the measured data of gyro. Then the model from the data based on the GP was established and the nonlinear mathematic relationship between the current time and the former times was obtained. Finally, GA was used to optimize the parameters of the mathematic relationship in order to get the more accurate model. The experiment result indicates that compared with classical auto regressive (AR) model, this model can effectively reflect the characteristics of gyro's random drift. The square error of the gyro's random drift has decreased by 73.72% and the effects have increased by 4.72% compared with classical AR model. The establishing model method effectively compensates the gyro's random drift and improved the stability of the system.

Key words: gyroscope, genetic programming (GP), genetic algorithm (GA), modeling of random drift, optimizing

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