基于Elman神经网络的GNSS/INS全域高精度定位方法

针对当前智能网联汽车定位与导航系统无法接收全球导航卫星系统(GNSS)信号引起定位失效的问题,提出一种基于Elman神经网络的GNSS结合惯性导航系统(INS)的全域高精度定位方法。首先,采用神经网络方法,建立了基于Elman网络的GNSS/INS高精度定位训练模型和GNSS失效预测模型;然后,利用GNSS、INS和实时动态(RTK)等定位技术,设计了GNSS/INS高精度定位数据采集实验系统;最后,选取采集的有效实验数据进行了反向传播(BP)神经网络、级联BP(CFBP)神经网络、Elman神经网络的训练模型性能对比分析,并验证了基于Elman网络的GNSS失效预测模型。实验结果表明,所提方法训练误差指标均优于基于BP和CFBP神经网络的方法;在GNSS失效1 min、2 min、5 min时,基于预测模型的预测平均绝对误差(MAE)、方差(VAR)和均方根误差(RMSE)分别为18.88 cm、19.29 cm、58.83 cm,8.96、8.45、5.68和20.90、21.06、59.10,随着GNSS信号失效时长的增加,定位预测精度降低。

GNSS/INS global high-precision positioning method based on Elman neural network

Aiming at positioning failure occured when positioning and navigation system of the intelligent connected vehicle fail to receive the signal of Global Navigation Satellite System (GNSS), a GNSS/Inertial Navigation System (INS) global high-precision positioning method based on Elman neural network was proposed. Firstly, a GNSS/INS high-precision positioning training model and a GNSS failure prediction model based on Elman neural network were established. Then, by using GNSS, INS and Real-Time Kinematic (RTK) and other positioning techniques, a data acquisition experiment system of GNSS/INS high-precision positioning was designed. Finally, the effective experimental data were collected to compare the performance of the training model of Back Propagation (BP) neural network, Cased-Forward BP (CFBP) neural network, Elman neural network, and the prediction model of GNSS signal outage based on Elman network was verified. The experimental results show that the training error of GNSS/INS prediction model based on Elman network is better than those based on BP and CFBP neural networks. When GNSS fails for 1 min, 2 min and 5 min, the prediction Mean Absolute Error (MAE), Variance (VAR) and Root Mean Square Error (RMSE) were 18.88 cm, 19.29 cm, 58.83 cm and 8.96, 8.45, 5.68 and 20.90, 21.06, 59.10 respectively, and with the increase of GNSS signal outage time, the positioning prediction accuracy is reduced.