中国惯性技术学报 ›› 2019, Vol. 27 ›› Issue (1): 15-22.doi: 10.13695/j.cnki.12-1222/o3.2019.01.003

• • 上一篇    

基于改进径向基神经网络的MEMS惯导系统误差抑制方法

陈光武,李文元,于 月,刘孝博   

  1. 兰州交通大学 自动控制研究所
  • 出版日期:2019-01-16 发布日期:2019-05-07

Error suppression method of MEMS inertial navigation system based on improved radial basis neural network

CHEN Guangwu, LI Wenyuan, YU Yue, LIU Xiaobo   

  1. Automatic Control Research Institute, Lanzhou Jiaotong University
  • Online:2019-01-16 Published:2019-05-07

摘要: 由微机电惯性导航系统和全球定位系统构成的组合导航系统在卫导信号失锁的情况下,纯惯导定位误差将迅速发散。为了抑制惯导系统误差发散,提出了改进的径向基神经网络与自适应卡尔曼滤波算法,并提出了新的网络训练模型,采用自适应量子粒子群算法改进径向基神经网络的结构设计与参数。在卫导信号可用时用组合导航数据训练神经网络,当卫导信号失锁时,由改进的径向基神经网络预测自适应卡尔曼滤波的量测,使滤波器继续为系统提供速度与位置修正值。实验结果表明,转弯行驶状态下,卫星失锁15 s时,相比较原算法,水平定位精度提高了62%,有效抑制了惯导误差。

关键词: 惯性导航系统, 误差抑制, 自适应量子粒子群, 径向基神经网络, 自适应卡尔曼滤波

Abstract: In the MEMS/GPS integrated navigation system, the positioning error of pure inertial navigation will quickly diverge during GPS outages. In order to suppress the error divergence, an improved radial basis neural network and an adaptive Kalman filter algorithm are proposed, and a new network training model is introduced. The adaptive quantum particle swarm optimization algorithm is used to improve the structural design and the parameters of radial basis neural network. The neural network is trained with the combined navigation data when the GPS signal is available. When the GPS outage occurs, the measurement of AKF is predicted by the improved radial basis neural network, so that the error of speed and position are provided continuously for the system. Experimental results show that when the GPS outage is up to 15 s, the horizontal positioning accuracy is improved by 62% compared with that of the original algorithm under the state of turning, which effectively suppresses the inertial navigation error.

Key words: inertial navigation system, error restraining, adaptive quantum particle swan optimization, radial basis function neural network, adaptive Kalman filtering