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

doi:10.11772/j.issn.1001-9081.2018091920

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (4): 994-1000.DOI: 10.11772/j.issn.1001-9081.2018091920

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

DENG Tianmin^1,2, FANG Fang¹, YUE Yunxia¹, YANG Qizhi¹

1. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China;
2. Chongqing Key Laboratory of"Human-Vehicle-Road"Cooperation & Safety for Mountain Complex Environment(Chongqing Jiaotong University), Chongqing 400074, China

Received:2018-09-14 Revised:2018-10-18 Online:2019-04-10 Published:2019-04-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51678099), the Science and Technology Talents Training Program of Chongqing Science & Technology Commission (CSTC2013 KJRC-QNRC0148).

基于Elman神经网络的GNSS/INS全域高精度定位方法

邓天民^1,2, 方芳¹, 岳云霞¹, 杨其芝¹

1. 重庆交通大学交通运输学院, 重庆 400074;
2. 山区复杂道路环境"人-车-路"协同与安全重庆市重点实验室(重庆交通大学), 重庆 400074

通讯作者: 邓天民
作者简介:邓天民(1979-),男,四川阆中人,副教授,博士,主要研究方向:交通大数据、自动驾驶、交通控制;方芳(1994-),女,四川自贡人,硕士研究生,主要研究方向:人工智能、交通信息与控制;岳云霞(1995-),女,内蒙古巴彦淖尔人,硕士研究生,主要研究方向:交通大数据;杨其芝(1994-),女,山东滕州人,硕士研究生,主要研究方向:交通大数据。
基金资助:
国家自然科学基金资助项目（51678099）；重庆市科学技术委员会科技人才培养计划项目（CSTC2013KJRC-QNRC0148）。

Abstract

Abstract: 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.

Key words: intelligent connected vehicle, global high-precision positioning, Global Navigation Satellite System (GNSS), signal outage, Elman neural network, data-driven

摘要： 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。

关键词: 智能网联汽车, 全域高精度定位, 全球导航卫星系统, 信号失效, Elman神经网络, 数据驱动

CLC Number:

DENG Tianmin, FANG Fang, YUE Yunxia, YANG Qizhi. GNSS/INS global high-precision positioning method based on Elman neural network[J]. Journal of Computer Applications, 2019, 39(4): 994-1000.

邓天民, 方芳, 岳云霞, 杨其芝. 基于Elman神经网络的GNSS/INS全域高精度定位方法[J]. 计算机应用, 2019, 39(4): 994-1000.

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

基于Elman神经网络的GNSS/INS全域高精度定位方法

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