An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

IF 2 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Physical Communication Pub Date : 2024-08-21 DOI:10.1016/j.phycom.2024.102475
Mostafa Mohamed Ahmed , Mahmoud A. Shawky , Shady Zahran , Adel Moussa , Naser EL-Shimy , Adham A. Elmahallawy , Shuja Ansari , Syed Tariq Shah , Ahmed Gamal Abdellatif
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Abstract

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

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通过多样化估算器和人群感应数据融合实现室外无人机定位的实验分析
在全球导航卫星系统(GNSS)屏蔽的环境中,无人驾驶飞行器(UAV)要实现精确的三维室外定位,这是一项挑战,受此激励,本文提出了一种创新技术。在全球导航卫星系统(GNSS)信号中断期间,所提出的方法整合了人群感应数据融合以应对惯性导航系统(INS)漂移,并利用不同的估计器来提高其功效。安装在无人机上的微型轻量级频率调制连续波(FMCW)雷达捕捉地面散射体的反射,通过快速傅里叶变换(FFT)处理生成测距-多普勒图。该图有助于在全球导航卫星系统信号丢失时估算前向速度。这种方法采用自适应阈值、图像二值化和基于连接组件的技术,从计算机视觉角度进行目标检测。利用扩展卡尔曼滤波器(EKF)和粒子滤波器(PF)等不同的估算器,将基于雷达的速度与磁力计、气压计和惯性测量单元(IMU)数据融合。使用 EKF 和 PF 进行的实时飞行数据评估和模拟中断期验证了室外定位系统。实验分析表明,利用粒子滤波器在全球导航卫星系统(GNSS)信号丢失时强化独立 INS 模式,可大幅提高三维定位精度,首次飞行和后续飞行的定位精度分别提高了 99.89% 和 99.83%。这种方法大大提高了无人飞行器的定位精度,尤其是在具有挑战性的全球导航卫星系统失效情况下,展示了其在现实世界中的应用潜力。
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来源期刊
Physical Communication
Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS
CiteScore
5.00
自引率
9.10%
发文量
212
审稿时长
55 days
期刊介绍: PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.
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