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Aerospace
Special Issues
Global Navigation Satellite System for Unmanned Aerial Vehicle
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Global Navigation Satellite System for Unmanned Aerial Vehicle

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3258

Special Issue Editors

Dr. Guohao Zhang

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: GNSS positioning in challenging environments; 3D-mapping-aided GNSS; collaborative positioning; signal propagation modelling; multi-sensor integration; machine-learning-aided positioning; indoor positioning
Dr. Weisong Wen

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Interests: GNSS; GNSS-RTK; GNSS NLOS/multipath mitigation in urban canyons LiDAR-aided GNSS positioning; perception-aided GNSS positioning; LiDAR SLAM in challenging dynamic scenes; navigation; autonomous driving; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With excellent flexibility and mobility, the unmanned aerial vehicle (UAV) has received extensive attention and rapid developments in the last few decades, contributing to various applications, including surveillance, surveying, and future intelligent transportation systems. The effectiveness of those applications greatly relies on the performance of the onboard positioning system. UAV positioning is usually achieved by the global navigation satellite system (GNSS) due to its wide availability of service, absolute positioning with sufficient accuracy, and acceptable cost for the mass market. With the increasing trend of using UAVs for civil applications, there are new demands on their GNSS performance. The positioning solution is required to be accurate and precise, even for urban areas, where the multipath and non-line-of-sight signal receptions are challenging for GNSS. In addition, the reliability of GNSS has also attracted concerns about guaranteeing the safe operation of UAVs, especially with the recent security threats from spoofing. The development of UAVs onboard GNSS also enables its integration with cutting-edge technologies from other research fields. 

This Special Issue invites recent contributions related to the different aspects of GNSS for UAVs, including but not limited to: positioning in challenging environments, advanced estimation techniques, filtering and optimization methods for positioning such as Kalman filter and factor graph optimization, fault detection and exclusion, GNSS precise positioning, signal propagation modelling and simulation, cooperative positioning, machine learning or deep-learning-aided GNSS, GNSS-involved multi-sensor integrated system, GNSS-integrated simultaneous localization and mapping (SLAM), GNSS-related UAV control or path planning, GNSS spoofing detection, and other cutting-edge techniques such as 3D-mapping-aided GNSS. 

Dr. Guohao Zhang
Dr. Weisong Wen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV
  • GNSS
  • urban positioning
  • multipath
  • Kalman filter
  • factor graph
  • fault detection and isolation
  • precise positioning
  • simultaneously localization and mapping (SLAM)
  • cooperative positioning
  • machine learning
  • multi-sensor integration
  • spoofing detection

Published Papers (2 papers)

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Research

24 pages, 7588 KiB  
Article
A Cooperative Target Localization Method Based on UAV Aerial Images
by Minglei Du, Haodong Zou, Tinghui Wang and Ke Zhu
Aerospace 2023, 10(11), 943; https://doi.org/10.3390/aerospace10110943 - 6 Nov 2023
Cited by 1 | Viewed by 1255
Abstract
A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect [...] Read more.
A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target. Full article
(This article belongs to the Special Issue Global Navigation Satellite System for Unmanned Aerial Vehicle)
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36 pages, 6949 KiB  
Article
AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks
by Anirudh Warrier, Saba Al-Rubaye, Gokhan Inalhan and Antonios Tsourdos
Aerospace 2023, 10(10), 884; https://doi.org/10.3390/aerospace10100884 - 13 Oct 2023
Viewed by 1522
Abstract
Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel [...] Read more.
Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field. Full article
(This article belongs to the Special Issue Global Navigation Satellite System for Unmanned Aerial Vehicle)
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