神经网络和滞后变量回归的车载终端误差修正  

Enhancing Vehicle Terminal Positioning Accuracy Using Neural Network and Lagged Variable Regression

作　　者：李阳 张建[1] 程序 LI Yang;ZHANG Jian;CHENG Xu(Jiangsu Institute of Metrology,Nanjing 210023,China)

机构地区：[1]江苏省计量科学研究院,南京210023

出　　处：《计量科学与技术》2023年第8期48-53,共6页Metrology Science and Technology

基　　金：江苏省市场监督管理局科技计划项目(KJ2022014)。

摘　　要：针对现有车载定位终端存在定位误差大和更新速度慢的问题,深入分析了车载终端定位误差的影响因素,并提出了基于BP神经网络和滞后变量回归的车载终端定位误差修正方法。对比车载终端三次测量数据修正前后的定位误差,最大定位误差分别减小了88.2%、85.4%和85.8%。通过实测数据对比了车载终端修正前后的定位误差,证明了使用BP神经网络和滞后变量回归建立的车载终端定位误差模型是有效的,定位误差修正效果较好。The existing onboard positioning terminals face challenges of significant positioning errors and slow update speeds.This paper analyzes the factors influencing these errors and proposes a method for correcting vehicle terminal positioning errors using a BP neural network and lagged variable regression.Comparative analysis of three measurement data sets before and after correction shows maximum positioning error reductions of 88.2%,85.4%,and 85.8%,respectively.Additionally,the comparison of pre-and post-correction positioning errors with measured data validates the effectiveness of the developed model utilizing BP neural networks and lagged variable regression for positioning error correction.

关 键 词：计量学 定位误差修正 BP神经网络 滞后变量回归 道路运输车辆 车载终端 

分 类 号：TB973[一般工业技术—计量学]