摘要
针对组合导航姿态估计中无味四元数估计(unscented quaternion estimation,USQUE)的噪声协方差矩阵参数无法准确给出等问题,提出基于粒子群优化的USQUE(USQUE based on particle swarm optimization,PSO-USQUE)算法。通过粒子群算法对噪声协方差矩阵Q和R进行寻优,获取优化的噪声协方差矩阵等滤波先验条件;分别进行仿真实验和微机电惯导系统/GPS车载实验。实验结果表明,对于USQUE的姿态估计问题,PSO-USQUE算法相比常规算法具有更高的精度,验证了所提算法的有效性。
Aiming at the problem that the noise covariance matrix parameters of the unscented quaternion estimation(USQUE)in integrated navigation attitude estimation cannot be given accurately,an USQUE based on particle swarm optimization(PSO-USQUE)algorithm is proposed.PSO is used to optimize the noise covariance matrices Qand R,and the filtering prior conditions such as the optimized noise covariance matrix are obtained.In order to verify the effectiveness of the algorithm proposed,simulation tests and micro-electromechonical system(MEMS)inertial navigation system/GPS vehicle tests are performed separately.The experimental results show that the PSO-USQUE algorithm has higher accuracy than the conventional algorithm for the attitude estimation problem of USQUE.
作者
吕旭
胡柏青
戴永彬
赵仁杰
LYU Xu;HU Baiqing;DAI Yongbin;ZHAO Renjie(College of Electrical Engineering,Naval University of Engineering,Wuhan 430033,China;School of Electrical Engineering,Liaoning University of Technology,Jinzhou 121001,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2020年第6期1366-1371,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61703419)资助课题。
关键词
粒子群优化
四元数
卡尔曼滤波
组合导航
姿态估计
particle swarm optimization(PSO)
quaternion
Kalman filter
integrated navigation
attitude estimation
