面向无人智能小车的双验证安全定位方法

顾晓丹; 夏国正; 宋炳辰; 杨明; 罗军舟

doi:10.11959/j.issn.1000-436x.2024038

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面向无人智能小车的双验证安全定位方法

学术论文 | 更新时间：2024-08-08

- 面向无人智能小车的双验证安全定位方法
- Dual-verified secure localization method for unmanned intelligent vehicles
- 通信学报 2024年45卷第6期页码：131-143
- 作者机构：
  
  东南大学计算机科学与工程学院，江苏南京 211189
- 作者简介：
  
  [ "顾晓丹（1987- ），女，江苏常州人，博士，东南大学讲师，主要研究方向为移动互联网、网络安全、匿名通信等。" ]
  [ "夏国正（1996- ），男，江苏南通人，东南大学硕士生，主要研究方向为物联网、无线网络、网络安全等。" ]
  [ "宋炳辰（1999- ），男，黑龙江肇东人，东南大学博士生，主要研究方向为物联网、网络安全等。" ]
  [ "杨明（1979- ），男，江苏常州人，东南大学教授、博士生导师，主要研究方向为网络安全与隐私保护、物联网等。" ]
  [ "罗军舟（1960- ），男，浙江宁波人，东南大学教授、博士生导师，主要研究方向为未来网络体系结构、协议工程、网络安全、无线网络、云计算与大数据等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62072102;62132009;62102084)
- DOI：10.11959/j.issn.1000-436x.2024038
  中图分类号： TP393
- 收稿日期：2023-10-31，
  
  修回日期：2024-02-02，
  
  纸质出版日期：2024-06-25
- 稿件说明：
移动端阅览
顾晓丹,夏国正,宋炳辰等.面向无人智能小车的双验证安全定位方法[J].通信学报,2024,45(06):131-143.

GU Xiaodan,XIA Guozheng,SONG Bingchen,et al.Dual-verified secure localization method for unmanned intelligent vehicles[J].Journal on Communications,2024,45(06):131-143.
顾晓丹,夏国正,宋炳辰等.面向无人智能小车的双验证安全定位方法[J].通信学报,2024,45(06):131-143. DOI： 10.11959/j.issn.1000-436x.2024038.

GU Xiaodan,XIA Guozheng,SONG Bingchen,et al.Dual-verified secure localization method for unmanned intelligent vehicles[J].Journal on Communications,2024,45(06):131-143. DOI： 10.11959/j.issn.1000-436x.2024038.

摘要

针对无人智能小车在网络、硬件、操作系统和软件方面存在众多安全隐患，易受到物理或远程安全攻击，使其偏离配送轨迹导致配送任务失败，甚至被攻击者操控干扰工厂正常运行的问题，提出了一种面向无人智能小车的双验证安全定位方法。在无人智能小车端，利用已有的Wi-Fi网络基础设施进行指纹定位，并设计特征融合策略实现Wi-Fi和磁场指纹的动态融合；在环境端，部署多个监测点采集无人智能小车发出的声音信号计算到达时间差，并根据空间分割方法计算小车位置。在此基础上，通过将无人智能小车上报的位置信息和监测点计算的位置坐标进行对比验证，一旦发现小车位置出现异常则进行异常告警，从而保证无人智能小车的正常运转工作。在真实室内场景下的实验结果表明，所提方法可以有效跟踪目标设备的位置坐标，定位精度优于现有基准算法。

Abstract

Unmanned intelligent vehicles are exposed to high risks of network attack

hardware attack

operating system attack and software attack. They are susceptible to physical or remote security attacks

causing it to deviate from the delivery trajectory and fail the delivery task

or even be manipulated to disrupt normal operation of the factory. To address this problem

a dual-verified secure localization method for unmanned intelligent vehicles was proposed. The existing Wi-Fi network infrastructure was utilized by the vehicles for fingerprinting localization and a feature fusion strategy was designed to realize the dynamic fusion of Wi-Fi and magnetic field fingerprints. Multiple environmental monitoring points were deployed to collect the sound signals made by vehicles to calculate the position based on time difference of arrival and spatial segmentation method. Then the location reported by the vehicle was compared with the result of monitoring points for verification. Once an abnormal position was detected

an alert would be issued

ensuring the normal operation of the unmanned intelligent vehicles. The experimental results in the real indoor scenarios show that the proposed method can effectively track the positions of the target unmanned intelligent vehicle

and the positioning accuracy is better than existing benchmark algorithms.

关键词

Keywords

references

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