巡飞弹空中自适应快速初始姿态估计

李增彦; 李小民; 刘秋生; 周兆英

doi:10.3788/OPE.20172402.0493

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巡飞弹空中自适应快速初始姿态估计

信息科学 | 更新时间：2020-07-07

- 巡飞弹空中自适应快速初始姿态估计
- Adaptive fast initial attitude estimation for inflight loitering munition
- 光学精密工程 2017年25卷第2期页码：493-501
- 作者机构：
  
  1.军械工程学院无人机工程系, 河北石家庄 050003
  2.军械工程学院弹药工程系, 河北石家庄 050003
  3.清华大学精密仪器系, 北京 100084
- 作者简介：
  
  李增彦 (1987-), 男, 河北石家庄人, 博士研究生, 2012年于军械工程学院获得硕士学位, 现为军械工程学院博士研究生, 主要从事无人机测控与飞行控制方面的研究。E-mail:Lizy_THU@163.com LI Zeng-yan, E-mail: Lizy_THU@163.com
  [ "李小民 (1968-)，男，河北保定人，博士，教授，博士生导师，主要从事无人机测控与飞行控制方面的研究。E-mail: lxmfy2000@263.net" ]
- 基金信息：
  
  武器装备“十二五”预先研究项目(51325050101)
- DOI：10.3788/OPE.20172402.0493
  中图分类号： V249
- 收稿日期：2016-09-23，
  
  录用日期：2016-11-4，
  
  纸质出版日期：2017-02-25
- 稿件说明：
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李增彦, 李小民, 刘秋生, 等. 巡飞弹空中自适应快速初始姿态估计[J]. 光学精密工程, 2017,25(2):493-501.

Zeng-yan LI, Xiao-min LI, Qiu-sheng LIU, et al. Adaptive fast initial attitude estimation for inflight loitering munition[J]. Optics and precision engineering, 2017, 25(2): 493-501.
李增彦, 李小民, 刘秋生, 等. 巡飞弹空中自适应快速初始姿态估计[J]. 光学精密工程, 2017,25(2):493-501. DOI： 10.3788/OPE.20172402.0493.

Zeng-yan LI, Xiao-min LI, Qiu-sheng LIU, et al. Adaptive fast initial attitude estimation for inflight loitering munition[J]. Optics and precision engineering, 2017, 25(2): 493-501. DOI： 10.3788/OPE.20172402.0493.

摘要

为了解决巡飞弹空中上电后在无参考姿态条件下的初始姿态确定问题，采用低成本磁力计、陀螺仪和加速度计（MARG）传感器设计姿态航向参考系统（AHRS），并提出了一种自适应参考矢量权重的快速初始姿态估计（AFCF）算法。首先，提出了三轴传感器使用前的快速误差校准方法；然后，采用快速互补滤波算法进行姿态估计，分析了其权重函数对于初始姿态估计及收敛性等的影响；接着，提出自适应参考矢量权重及自适应姿态估计方法；最后，利用高精度MTI（Milliren Technologies，Inc）传感器数据对算法进行了验证，并在低成本MARG姿态航向参考系统中对算法进行了实现，对比了改进算法及扩展卡尔曼滤波（EKF）算法的性能。实验结果与分析表明：动态条件下采用MTI传感器数据，改进算法能够在初始时刻收敛，比快速互补滤波（FCF）算法提前约4s；解算精度约为±0.6°，初始时刻精度明显优于FCF；硬件测试则表明改进算法的处理时间为0.062ms，仅为EKF算法的1/9，解算精度约为±1.3°，能够满足姿态测量过程快速收敛、高精度、实时性等要求。

Abstract

To solve the problem of initial attitude estimation for inflight loitering munition in the absence of reference attitude

a fast initial attitude estimation algorithm for adaptive reference vector weight (AFCF) was put forward based on the Attitude and Heading Reference System (AHRS) designed by adopting low-cost magnetic

angular rate and gravity MARG sensor. First of all

a fast error calibration method for three-axes sensor was put forward; Then

the attitude estimation was carried out by adopting the fast complementary filtering algorithm

and the impact of weighting function on initial attitude estimation and convergence was analyzed; subsequently the method for adaptive reference vector weight and adaptive attitude estimation was proposed; finally

high-precision MTI sensor data was used to verify the algorithm

then the algorithm was implemented in the low-cost MRAG AHRS

and performance of the improved algorithm was compared with that of the extended Kalman filter (EKF) algorithm. The experiment results and analysis show that the improved algorithm can achieve a convergence at the initial time when MTI sensor data is used under dynamic conditions

approximately 4s earlier than the fast complement filter (FCF) algorithm; the calculation precision is ±0.6°

and the initial precision is obviously better than FCF.Furthermore

the hardware test indicates that processing time for the improved algorithm is 0.062 ms

accounting for 1/9 of the EKF algorithm

with an approximately calculation precision of ±1.3°

which can meet the requirement of fast convergence

high precision and real-time during the attitude measurement.

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references

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