车载终端校准系统设计及定位误差修正  

Design of Vehicle Terminal Calibration System and Positioning Error Correction

作　　者：张建[1] 李阳 程序 ZHANG Jian;LI Yang;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6期16-21,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“车载定位终端智能计量校准装置研究”(KJ2022014)。

摘　　要：近年来,道路运输业得到了快速发展,在提高车辆运输效率和运载能力的同时,也需要加强对运输车辆的安全管理,实时监控其行驶路线和行驶状态,目前车载终端存在定位误差和信息更新缓慢等问题。研制了一套道路运输车辆卫星定位终端计量标准系统,通过同时测量定位终端计量标准系统与车载终端在不同速度下的位移信息和速度信息,得到车载终端的定位误差。提出了基于遗传算法和BP神经网络相结合的道路运输车辆车载终端定位误差修正方法,对比车载终端3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82.79%、87.95%和89.55%。实验结果表明,利用BP神经网络建立的车载终端定位误差模型是有效的,定位误差修正效果良好。In recent years,the road transportation industry has witnessed significant advancements.While there have been improvements in vehicle transport efficiency and cargo capacity,there is a paramount need to enhance the safety management of transport vehicles by monitoring their routes and conditions in real time.Current vehicle terminal calibration systems exhibit significant positioning errors and sluggish information updates.This paper introduces a developed calibration system for satellite positioning of road transport vehicles.Through simultaneous measurement of displacement and speed data from both the calibration system and the vehicle terminal at varying speeds,the positioning discrepancies of the vehicle terminal were discerned.An innovative correction method,integrating genetic algorithms with a BP neural network,was proposed to rectify these errors.By comparing positioning data pre and post-correction,we observed maximum error reductions of 82.79%,87.95%,and 89.55% respectively.Experimental outcomes affirm the efficacy of the BP neural network-based positioning error model,demonstrating substantial error correction capabilities.

关 键 词：计量学 道路运输车辆 车载终端 遗传算法 BP神经网络 定位误差修正 

分 类 号：TB973[一般工业技术—计量学]