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

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.