面向自动驾驶的车辆精确实时定位算法

沈连丰; 张瑞; 朱亚萍; 吴怡

doi:10.11999/JEIT190610

面向自动驾驶的车辆精确实时定位算法

doi: 10.11999/JEIT190610

沈连丰^1, ,,
张瑞¹,
朱亚萍¹,
吴怡²

1.
东南大学移动通信国家重点实验室南京 210096
2.
福建师范大学光电与信息工程学院福州 350007

基金项目: 国家自然科学基金(61601122, 61741102, U180526, 61571128)

详细信息

作者简介:
沈连丰：男，1952年生，教授，主要研究方向为宽带移动通信、泛在网络和车辆自组织网络等

张瑞：男，1986年生，博士生，研究方向为短距无线通信、车辆自组织网络

朱亚萍：女，1990年生，博士生，研究方向为短距无线通信、软件定义传感器网络

吴怡：女，1970年生，教授，主要研究方向为通信与信息系统，车辆自组织网络等

通讯作者:
沈连丰　lfshen@seu.edu.cn

中图分类号: TN953; TP872
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出版历程
- 收稿日期: 2019-08-12
- 修回日期: 2019-11-21
- 网络出版日期: 2019-12-04
- 刊出日期: 2020-01-21

High-precision and Real-time Localization Algorithm for Automatic Driving Vehicles

1.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2.
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China

Funds: The National Natural Science Foundation of China (61601122, 61741102, U180526, 61571128)

摘要

摘要: 针对车辆自组织网络(VANETs)中的车辆定位问题，以提高定位精度和实时性为目标，该文提出一种面向自动驾驶的车辆精确实时定位算法，包括基于矩阵束(MP)与非线性拟合(NLF)以及基于视觉感知两种技术。基于MP-NLF的技术通过联合TOA/AOA估计进行车辆单站定位，并引入高分辨率估计以提高估计精度；基于视觉感知的技术通过提取定位范围内视觉感知图像的特征信息来完成定位，并结合惯性信息进行无迹卡尔曼滤波进一步提高精度。仿真结果表明，与传统多径指纹算法相比，所提算法即使在低信噪比情况下也具有较好的定位性能。
- 车辆自组织网络 /
- 定位 /
- 路边单元 /
- 高分辨率估计
Abstract: For the problem of vehicle positioning in Vehicular Ad-hoc NETworks (VANETs), in order to improve the positioning accuracy and real-time performance, a high-precision and real-time localization algorithm for automatic driving vehicles is proposed, including two technologies based on Matrix Pencil (MP) and Non-Linear Fitting (NLF), and visual perception. The MP-NLF technology uses joint TOA/AOA estimation to locate vehicles with a single station, and introduces high resolution estimation technology to improve the estimation accuracy. The visual perception based technology completes the localization by extracting the feature information of visual perceptual images in positioning area, carries on the unscented Kalman filter combined with the inertial sensor information to further improve the positioning accuracy. The simulation results show that, compared with the traditional multipath fingerprinting algorithm, the proposed algorithm has better performance even in the case of low Signal-to-Noise Ratio (SNR).
- Vehicular Ad-hoc NETworks (VANETs) /
- Positioning /
- Roadside unit /
- High-resolution estimation

HTML全文

图 1 VANETs车辆定位场景示意图

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图 2 车辆端ULA接收多径信号示意图

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图 3 车辆端ULA接收示意图

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图 4 车辆视觉感知定位示意图

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图 5 VANETs车辆定位仿真场景图

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图 6 车辆行驶过程中所提算法与SP算法的均方根误差比较

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图 7 不同阵元数目下所提算法与SP算法定位误差的CDF分布(B_w = 10 MHz)

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图 8 不同信号带宽下所提算法与SP算法定位误差的CDF分布(M = 8)

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表 1 系统仿真参数设置

仿真参数	参数值
OFDM子载波数目	K = 16
ULA阵元数目M	4/6/8/10/12
信号带宽B_w (MHz)	5/10/20
SP算法中每次快拍的采样数	N_s = 8
SP算法中数据点的快拍数	L_d = 50
SP算法中测试点的快拍数	L_t = 20

下载: 导出CSV

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