基于AEKF的高速自旋飞行体组合导航方法

doi:10.12305/j.issn.1001-506X.2022.06.25

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (6): 1977-1983.doi: 10.12305/j.issn.1001-506X.2022.06.25

基于AEKF的高速自旋飞行体组合导航方法

董一平^1,², 刘宁^1,^2,*, 苏中^1,², 王靖骁^1,², 白宏阳³

1. 北京信息科技大学自动化学院, 北京 100192
2. 北京信息科技大学高动态导航技术北京市重点实验室, 北京 100192
3. 南京理工大学能源与动力工程学院, 南京 210094

收稿日期:2021-07-22 出版日期:2022-05-30 发布日期:2022-05-30
通讯作者: 刘宁
作者简介:董一平(1997—), 女, 硕士研究生, 主要研究方向为高动态导航技术、组合导航|刘宁(1986—), 男, 副研究员, 博士, 主要研究方向为高动态谐振陀螺、导航方法|苏中(1962—), 男, 教授, 博士, 主要研究方向为惯性器件和高动态惯性测量单元|王靖骁(1998—), 男, 硕士研究生, 主要研究方向为惯性器件智能误差补偿、智能导航|白宏阳(1985—), 男, 副教授, 博士, 主要研究方向为导航制导与控制、组合导航
基金资助:
北京市自然科学基金(4212003);国家自然科学基金(61801032);高动态导航技术北京市重点实验室资助课题

Integrated navigation method of high-speed spinning flying bodybased on AEKF

Yiping DONG^1,², Ning LIU^1,^2,*, Zhong SU^1,², Jingxiao WANG^1,², Hongyang BAI³

1. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
2. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science andTechnology University, Beijing 100192, China
3. School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2021-07-22 Online:2022-05-30 Published:2022-05-30
Contact: Ning LIU

摘要/Abstract

摘要：

针对高速自旋飞行体运行过程中噪声特性无法准确获取的问题, 提出了基于改进自适应扩展卡尔曼滤波(adaptive extended Kalman filter, AEKF)算法对量测噪声进行自适应调节, 并在扩展卡尔曼滤波(extended Kalman filter, EKF)的基础上提出了一种基于对状态变量新的建模方式的EKF算法, 提高算法的实时性。采用北斗/捷联惯性导航系统(strapdown inertial navigation system, SINS)组合导航方案, 在EKF的基础上, 引入带遗忘因子的噪声估计器, 通过AEKF对组合导航数据进行融合, 对量测噪声进行估计。仿真结果表明, 所提出的组合导航方法对高速自旋飞行体的姿态和位置定位误差较小, 与无改进的AEKF相比, 具有更好的收敛性。

关键词: 高速自旋, 扩展卡尔曼滤波, 组合导航, 自适应扩展卡尔曼滤波, 遗忘因子

Abstract:

Aiming at the problem that the noise characteristics of high-speed spinning flying bodies can not be accurately acquired during operation, an improved adaptive extended Kalman filter (AEKF) algorithm is proposed to adaptively adjust the measurement noise, and an extended Kalman filter (EKF) algorithm based on a new modeling method of state variables is proposed based on the EKF to improve the real-time performance of the algorithm. The Beidou/strapdown inertial navigation system (SINS)integrated navigation scheme is adopted, and on the basis of EKF, a noise estimator with forgetting factor is introduced, and the integrated navigation data is fused through AEKF to estimate the measurement noise. The simulation results show that the proposed integrated navigation method has smaller positioning errors for the attitude and position of high-speed spinning flying objects, and has better convergence than the unmodified AEKF.

Key words: high-speed spin, extended Kalman filter (EKF), integrated navigation, adaptive extended Kalman filter (AEKF), forgetting factor

中图分类号:

V249.3

董一平, 刘宁, 苏中, 王靖骁, 白宏阳. 基于AEKF的高速自旋飞行体组合导航方法[J]. 系统工程与电子技术, 2022, 44(6): 1977-1983.

Yiping DONG, Ning LIU, Zhong SU, Jingxiao WANG, Hongyang BAI. Integrated navigation method of high-speed spinning flying bodybased on AEKF[J]. Systems Engineering and Electronics, 2022, 44(6): 1977-1983.

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参数	误差项	误差值
陀螺仪	零位稳定性/(°/h)	18
	标度因数非线性/ppm	3 200
	量程/(°/s)	±7 200
加速计	零位稳定性	12 mg
	标度因数非线性/ppm	1 500
	量程	±30 g
状态协方差矩阵P	位置/m²	0.001²
	速度/(m·s^-1)	0.001²
	姿态/rad	0.001 74²
过程噪声矩阵Q	加速度计	12² mg
过程噪声矩阵Q	陀螺仪/(°/h)	18²
量测噪声矩阵R	位置/m²	0.1²
量测噪声矩阵R	速度/(m·s^-1)	0.1²

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基于AEKF的高速自旋飞行体组合导航方法

Integrated navigation method of high-speed spinning flying bodybased on AEKF

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