基于改进样本卷积交互网络的车辆组合导航系统研究

doi:10.3969/j.issn.1674-8484.2025.02.015

摘要/Abstract

摘要：

车载全球卫星定位系统/惯性导航系统（GNSS/INS组合导航）的GNSS信号在信号遮蔽环境中容易失锁，导致定位结果发散，影响无人车行驶的效率和安全。针对这一问题，该研究提出一种基于改进样本卷积交互网络（SCINet）的人工智能解决方案。所提出的模型在低层数的SCINet基础上增加了主成分分析、趋势分解、线性卷积交互学习等策略，提高了模型在该工况下的工作稳定性和准确性。结果表明：所提出的模型与长短记忆网络（LSTM）和SCINet相比定位误差缩小了80.9%和67.6%，有效提高了GNSS失锁状态下无人车的室外定位精度，保证了无人车辆定位的可靠性和安全性。

关键词: 无人车, 组合导航, 惯性导航系统（INS）失锁, 样本卷积交互网络（SCINet）, 趋势分解

Abstract:

Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated navigation system in vehicles is prone to signal loss in obstructed environments, leading to divergent positioning results and compromising the efficiency and safety of unmanned vehicles. To address this issue, this study proposed an artificial intelligence solution based on an improved Sample Convolution and Interaction Network (SCINet), which incorporated strategies such as principal component analysis, trend decomposition, and linear convolutional interactive learning on a low-layer SCINet architecture, enhancing the stability and accuracy of the model under such operating conditions. The results show that the proposed model reduces positioning errors by 80.9% and 67.6% compared to Long Short-Term Memory (LSTM) and SCINet, respectively, effectively improving the outdoor positioning accuracy of unmanned vehicles during GNSS signal loss and ensuring the reliability and safety of unmanned vehicle positioning.

Key words: unmanned vehicles, integrated navigation, inertial navigation system (INS) outage, sample convolutional interaction network (SCINet), trend seasonal separating

中图分类号:

TP183

匡兴红, 严碧云. 基于改进样本卷积交互网络的车辆组合导航系统研究[J]. 汽车安全与节能学报, 2025, 16(2): 315-325.

KUANG Xinghong, YAN Biyun. Research on vehicle integrated navigation system based on improved sample convolutional interaction network[J]. Journal of Automotive Safety and Energy, 2025, 16(2): 315-325.

图/表 19

图1 组合导航系统的流程示意图

图2 SCINet的网络框架

图3 D-SCINet的网络框架

图4 训练模式

图5 预测模式

参数	精度
陀螺仪静止零漂	10 (°) / h
陀螺仪噪声	$60\left(^{\circ}\right) /\left[\mathrm{s}(\sqrt{\mathrm{~Hz}})^{-1}\right]$
陀螺仪采样频率	100 Hz
加速度计静止零漂	(±15) μg
加速度计噪声	60 μg / Hz^-2
加速度计采样频率	100 Hz
GNSS精度(水平)	0~2 m
GNSS采样频率	5 Hz

表1 公共数据集的传感器参数

图6 公共数据集的路线及数据集划分

图7 各模型在路线1的预测轨迹和预测水平位置误差

图8 各模型在路线1上预测值的水平位置误差分布

图9 路线1上的各神经网络输出

图10 各模型在路线2的预测轨迹和预测水平位置误差

图11 各模型在路线2上预测值的水平位置误差分布

图12 路线2上的各神经网络输出

参数	精度
陀螺仪静止零漂	±(0.5~1) (°) / s
陀螺仪RMS噪声	0.028~0.07 (°) / s
陀螺仪分辨率 (LSB)	0.06 (°) / s
陀螺仪采样频率	50 Hz
加速度计量程	(±16) g
加速度计静止零漂	±(20~40) mg
加速度计RMS噪声	0.75~1 mg/Hz
加速度计采样频率	50 Hz
GNSS精度	水平2.5 m
GNSS采样频率	1 Hz

表2 传感器参数

图13 训练路线与测试路线

图14 各方法的预测值对比结果

图15 各模型在私有数据集上的预测轨迹和绝对定位误差

图16 各神经网定位误差分布情况

模型	FLOPs/次	参数量	t_mean / ms
D-SCINet	5 491 200	28 976	19.2
SCINet-High-Level	11 625 984	73 172	69.8
SCINet-Low-Level	3 783 168	10 700	9.9

表3 模型复杂度和计算时间

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