RaDro: Indoor Drone Tracking Using Millimeter Wave Radar

Author:

Abdelnasser Heba1ORCID,Heggo Mohammad1ORCID,Pang Oscar2ORCID,Kovac Mirko2ORCID,McCann Julie A.1ORCID

Affiliation:

1. Department of Computing, Imperial College London, London, UK

2. Aerial Robotics Laboratory, Imperial College London, London, UK

Abstract

Core to drone design is its ability to ascertain its location by utilizing onboard inertial sensors combined with GPS data. However, GPS is not always reachable, especially in challenging environments such as indoors. This paper proposes RaDro; a system that leverages millimeter-waves (mmWave) to precisely localize and track drones in indoor environments. Unlike commonly used alternative technologies, RaDro is cost-effective and can penetrate obstacles, a bonus in non-line-of-sight (NLoS) scenarios, which enhances its reliability for tracking objects in complex environments. It does this without the need for tags or anchors to be attached to the drone, achieving 3D tracking with just a single radar point, significantly streamlining the deployment process. Comprehensive experiments are conducted in different scenarios to evaluate RaDro's performance. These include employing different drone models with different sizes to execute a range of aerial manoeuvres across different flight arenas, each with its own settings and clutter, and encountering various LoS and NLoS scenarios in dynamic environments. The experiments aimed to assess the capabilities of the system to extract coarse-grained and fine-grained information for drone detection, motion recognition, and localization. The results showcase precise localization, achieving a 50% reduction in localization error compared to the conventional baseline. This localization accuracy remains resilient even when confronted with interference from other moving sources. The results also demonstrate the system's ability to accurately localize drones in NLoS scenarios where existing state-of-the-art optical technologies cannot work.

Publisher

Association for Computing Machinery (ACM)

Reference61 articles.

1. UbiBreathe

2. Heba Abdelnasser and Julie A McCann. 2024. Drone Propeller Speed Measurement: Case Study using 5GHz RF and mmWave Radar. In IEEE 99th Vehicular Technology Conference: VTC2024-Spring.

3. WiGest: A ubiquitous WiFi-based gesture recognition system

4. Ganesan Balamurugan, J Valarmathi, and VPS Naidu. 2016. Survey on UAV navigation in GPS denied environments. In 2016 International conference on signal processing, communication, power and embedded system (SCOPES). IEEE, 198--204.

5. The probabilistic data association filter

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3