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

Abstract

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

CLC Number:

V249.3

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.

Figures/Tables 13

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

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