LSTM Neural Network Assisted GNSS/SINS Vehicle Positioning Based on Speed Classification

LI Hanxu; LI Xin; HUANG Guanwen; ZHANG Qin; CHEN Shipeng

doi:10.13203/j.whugis20230061

您当前的位置：

首页 >

文章列表页 >

LSTM Neural Network Assisted GNSS/SINS Vehicle Positioning Based on Speed Classification

Geodesy and Survey Engineering | 更新时间：2026-11-05

- LSTM Neural Network Assisted GNSS/SINS Vehicle Positioning Based on Speed Classification
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 7, Pages: 1311-1320(2025)
- 作者机构：
  
  1.长安大学地质工程与测绘学院，陕西西安，710054
  2.信息工程大学地理空间信息学院，河南郑州，450001
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20230061
  CLC： P228
- Received：25 October 2025，
  
  Published：05 July 2025
- 稿件说明：
移动端阅览
李晗旭,李昕,黄观文，等.速度分类的LSTM神经网络辅助GNSS/SINS车载定位方法[J].武汉大学学报（信息科学版）,2025,50(7):1311-1320.

LI Hanxu,LI Xin,HUANG Guanwen,et al.LSTM Neural Network Assisted GNSS/SINS Vehicle Positioning Based on Speed Classification[J].Geomatics and Information Science of Wuhan University,2025,50(7):1311-1320.
李晗旭,李昕,黄观文，等.速度分类的LSTM神经网络辅助GNSS/SINS车载定位方法[J].武汉大学学报（信息科学版）,2025,50(7):1311-1320. DOI：,10.13203/j.whugis20230061

LI Hanxu,LI Xin,HUANG Guanwen,et al.LSTM Neural Network Assisted GNSS/SINS Vehicle Positioning Based on Speed Classification[J].Geomatics and Information Science of Wuhan University,2025,50(7):1311-1320. DOI：,10.13203/j.whugis20230061

摘要

车辆非完整性约束(non-holonomic constraint，NHC)是车载全球导航卫星系统(global navigation satellite system，GNSS)/捷联惯性导航系统( strapdown inertial navigation system，SINS)组合定位常用的增强技术，应用机器学习的手段可以建立惯性测量单元输出与NHC伪观测量的复杂映射关系，从观测域直接调整NHC伪观测量大小，在一定程度上提高了传统NHC方法的约束精度。现有的机器学习方法没有考虑车辆运动状态影响，导致NHC预测精度和可靠性不高。最新研究表明机器学习可以预测车辆的前向速度，即虚拟里程计。然而，当前研究主要是将预测虚拟NHC和虚拟里程计分开讨论，没有充分挖掘二者之间的耦合关系以及三维速度对车辆进行全约束。研究了一种基于车辆速度分类的长短期记忆（long short-term memory，LSTM）神经网络用于车辆三维速度的预测，采用三维速度约束新息自适应调整其方差域，通过车载GNSS/SINS松组合验证了所提方法的有效性。实验结果表明，所提方法预测的前向速度的平均精度为0.4 m/s，侧向和垂向速度的平均精度分别为2.4、2.1 cm/s，在模拟GNSS信号完全缺失460 s时，三维速度约束的水平定位精度上相对于惯导推算结果改善了99.40%。

Abstract

Objectives

Vehicle non-holonomic constraint (NHC) is a commonly used enhancement technique for vehicle positioning global navigation satellite system (GNSS)/strapdown inertial navigation system (SINS) combination positioning. A complex mapping relationship between inertial measurement unit (IMU) output and NHC pseudo-observations can be established by applying machine learning methods

and the NHC pseudo-observations can be directly adjusted from the observation domain

which can improve the constraint accuracy compared with traditional NHC methods to a certain extent. Most existing machine learning methods do not consider the influence of vehicle motion state

resulting in low accuracy and reliability of NHC prediction. The latest researches show that machine learning can predict a vehicle forward speed

as a virtual odometer. However

the current research mainly discusses the prediction of virtual NHC and virtual odometer separately

and fails to fully explore the coupling relationship between them and the full constraint of three-dimensional velocity on vehicles.

Methods

We study a long short-term memory (LSTM) neural network based on vehicle speed classification for 3 dimensional (3D) vehicle speed prediction

and adaptively adjust its variance domain by using 3D speed residual. The vehicle GNSS/SINS positioning verifies the effectiveness of the proposed method.

Results and Conclusions

Experimental results show that the average accuracy of forward velocity predicted by the proposed method is 0.4 m/s

and the average accuracy of lateral and celestial velocity are 2.4 and 2.1 cm/s

respectively. The simulated GNSS signal is completely missing for 460 s

the horizontal positioning accuracy of 3D velocity constraint is improved by 99.40% compared with the inertial navigation results.

关键词

Keywords

references

雷哲哲，黄观文，杜源，等 . 低成本U-blox模块的单频GPS/BDS增强PPP定位性能分析［J］. 导航定位与授时， 2019 ， 6 （ 1 ）： 74 - 80 .

LEI Zhezhe ， HUANG Guanwen ， DU Yuan ， et al . Single Frequency GPS/BDS Enhanced PPP Positioning Performance Analysis of Low Cost U-blox Module ［J］. Navigation Positioning and Timing ， 2019 ， 6 （ 1 ）： 74 - 80 .

李彦杰，杨元喜，何海波 . 附加约束条件对GNSS/INS组合导航结果的影响分析［J］. 武汉大学学报（信息科学版）， 2017 ， 42 （ 9 ）： 1249 - 1255 .

LI Yanjie ， YANG Yuanxi ， HE Haibo . Effects Analysis of Constraints on GNSS/INS Integrated Navigation ［J］. Geomatics and Information Science of Wuhan University ， 2017 ， 42 （ 9 ）： 1249 - 1255 .

高周正 . 多模GNSS PPP/INS组合系统算法与应用研究［D］. 武汉：武汉大学， 2016 .

GAO Zhouzheng . Reserach on the Methodology and Applicationg of the Intergration Between the Multi-constellation GNSS PPP and Inertial Navigation System ［D］. Wuhan ： Wuhan University ， 2016 .

NIU X J ， NASSAR S ， EL-SHEIMY N . An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates ［J］. Navigation ， 2007 ， 54 （ 3 ）： 177 - 188 .

DISSANAYAKE G ， SUKKARIEH S ， NEBOT E ， et al . The Aiding of a Low-Cost Strapdown Inertial Measurement Unit Using Vehicle Model Constraints for Land Vehicle Applications ［J］. IEEE Transactions on Robotics and Automation ， 2001 ， 17 （ 5 ）： 731 - 747 .

NIU X J ， LI Y ， ZHANG Q ， et al . Observability Analysis of Non-holonomic Constraints for Land-Vehicle Navigation Systems ［J］. Journal of Global Positioning Systems ， 2012 ， 11 （ 1 ）： 80 - 88 .

邓玥，余江，郭文飞，等 . 信号遮挡环境下融合TOA/AOD的5G/SINS组合导航算法模型与精度分析［J］. 武汉大学学报（信息科学版）， 2022 ， 47 （ 7 ）： 1133 - 1139 .

DENG Yue ， YU Jiang ， GUO Wenfei ， et al . Modeling and Accuracy Analysis of TOA/AOD Based 5G/SINS Integrated Navigation in Case of Signal Blockage ［J］. Geomatics and Information Science of Wuhan University ， 2022 ， 47 （ 7 ）： 1133 - 1139 .

VENERI M ， MASSARO M . The Effect of Ackermann Steering on the Performance of Race Cars ［J］. Vehicle System Dynamics ， 2021 ， 59 （ 6 ）： 907 - 927 .

WEN Z Y ， YANG G L ， CAI Q Z . An Improved SINS/NHC Integrated Navigation Algorithm Based on Ackermann Turning Geometry ［J］. Measurement ， 2022 ， 192 ： 110859 .

CHEN Q J ， ZHANG Q ， NIU X J . Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS/INS Integrated System ［J］. IEEE Transactions on Intelligent Transportation Systems ， 2021 ， 22 （ 10 ）： 6503 - 6515 .

BROSSARD M ， BARRAU A ， BONNABEL S . AI-IMU Dead-Reckoning ［J］. IEEE Transactions on Intelligent Vehicles ， 2020 ， 5 （ 4 ）： 585 - 595 .

TANG H L ， NIU X J ， ZHANG T S ， et al . ODONet： Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer ［J］. IEEE Sensors Journal ， 2022 ， 22 （ 12 ）： 12197 - 12208 .

FEIGL T ， KRAM S ， WOLLER P ， et al . A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU ［C］// International Conference on Indoor Positioning and Indoor Navigation ， Pisa， Italy ， 2019 .

WAGSTAFF B ， KELLY J . LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation ［C］// International Conference on Indoor Positioning and Indoor Navigation ， Nantes， France ， 2018 .

SALEH K ， HOSSNY M ， NAHAVANDI S . Intent Prediction of Vulnerable Road Users from Motion Trajectories Using Stacked LSTM Network ［C］// The 20th International Conference on Intelligent Transportation Systems ， Yokohama，Japan ， 2017 .

GRAVES A ， MOHAMED A R ， HINTON G . Speech Recognition with Deep Recurrent Neural Networks ［C］// IEEE International Conference on Acoustics， Speech and Signal Processing ， Vancouver， BC， Canada， 2013 .

陆继翔，张琪培，杨志宏，等 . 基于CNN-LSTM混合神经网络模型的短期负荷预测方法［J］. 电力系统自动化， 2019 ， 43 （ 8 ）： 131 - 137 .

LU Jixiang ， ZHANG Qipei ， YANG Zhihong ， et al . Short-Term Load Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model ［J］. Automation of Electric Power Systems ， 2019 ， 43 （ 8 ）： 131 - 137 .

SKOG I ， HÄNDEL P ， NILSSON J O ， et al . Zero-Velocity Detection：An Algorithm Evaluation ［J］. IEEE Transactions on Bio-medical Engineering ， 2010 ， 57 （ 11 ）： 2657 - 2666 .

ZHANG X H ， ZHU F ， TAO X L ， et al . New Optimal Smoothing Scheme for Improving Relative and Absolute Accuracy of Tightly Coupled GNSS/SINS Integration ［J］. GPS Solutions ， 2017 ， 21 （ 3 ）： 861 - 872 .

严恭敏，翁浚 . 捷联惯导算法与组合导航原理［M］. 西安：西北工业大学出版社， 2019 .

YAN Gongmin ， WENG Jun . Strapdown Inertial Navigation Algorithm and Principle for Integrated Navigation ［M］. Xi’an ： Northwestern Polytechnical University Press ， 2019 .

杨元喜 . 自适应动态导航定位［M］. 北京：测绘出版社， 2006 .

YANG Yuanxi . Adaptive Navigation and Kinematic Positioning ［M］. Beijing ： Sino Maps Press ， 2006 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Machine Learning-Based Remote Sensing Retrieval of Soil Moisture： Advances and Challenges

A Method of Extracting Building Roof Edge Points from Airborne Point Clouds with Wide-Area Feature Optimization

An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification

Machine Learning Downscaling Methods for Inversion of Terrestrial Water Storage Anomalies in the Yangtze River Basin by GRACE

Quantitative Assessment of Natural and Human Activity Impacts on Spatial Network of Urban Thermal Environment in Resource-Depleted City

Related Author

ZHANG Liangpei

SHEN Huanfeng

LI Jie

YANG Qianqian

LI Zhenghao

YUAN Qiangqiang

JIANG Jinguang

HUO Pengpeng

Related Institution

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

School of Resources and Environmental Sciences, Wuhan University

Department of Geography, Faculty of Social Sciences, Hong Kong Baptist University

School of Geodesy and Geomatics, Wuhan University

GNSS Research Center, Wuhan University

AI问答

⁰