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。
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.
作者
董一平
刘宁
苏中
王靖骁
白宏阳
DONG Yiping;LIU Ning;SU Zhong;WANG Jingxiao;BAI Hongyang(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science and Technology University,Beijing 100192,China;School of Energy and Power Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2022年第6期1977-1983,共7页
Systems Engineering and Electronics
基金
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。
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
high-speed spin
extended Kalman filter(EKF)
integrated navigation
adaptive extended Kalman filter(AEKF)
forgetting factor
