Dual-verified secure localization method for unmanned intelligent vehicles

GU Xiaodan; XIA Guozheng; SONG Bingchen; YANG Ming; LUO Junzhou

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

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Dual-verified secure localization method for unmanned intelligent vehicles

Papers | 更新时间：2024-08-08

- Dual-verified secure localization method for unmanned intelligent vehicles
- Journal on Communications Vol. 45, Issue 6, Pages: 131-143(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62072102;62132009;62102084)
- DOI：10.11959/j.issn.1000-436x.2024038
  CLC： TP393
- Received：31 October 2023，
  
  Revised：2024-02-02，
  
  Published：25 June 2024
- 稿件说明：
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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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