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UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications
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UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2351

Special Issue Editors

Prof. Abdullah Kadri

E-Mail Website
Guest Editor
Department of Systems and Computer Engineering, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: Internet of Things; UAV-based communication; smart city applications; wireless communication and networks; hardware and embedded systems
Dr. Hakim Ghazzai

E-Mail Website
Guest Editor
Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Interests: smart city applications; artificial intelligence; Internet of Things; intelligent transportation systems; wireless communication

Special Issue Information

Dear Colleagues,

Over the last decade, UAVs have rapidly evolved into versatile platforms for mobile sensing, enabling real-time data acquisition across a broad spectrum of domains—spanning environmental monitoring, precision agriculture, infrastructure inspection, disaster response, traffic management and control, and public safety. This continuous advancement in technology opens doors to a multitude of new domains, such as innovative sensor integration, autonomous sensing and navigation, and collaborative and swarm UAV systems. Moreover, combining UAVs with other cutting-edge technologies, such as IoT and AI, will even facilitate the use of UAVs in more complex sensing systems in the future. Therefore, the purpose of this Special Issue is to collate original research and review articles on next-generation advances, innovations, emerging technologies, and future directions for deploying UAVs as mobile sensing platforms.

Prof. Abdullah Kadri
Dr. Hakim Ghazzai
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • mobile and remote collaborative sensing
  • data acquisition platform
  • data fusion
  • artificial intelligence
  • Internet-of-Things

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Published Papers (4 papers)

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Research

25 pages, 1418 KB  
Article
Artificial Intelligence-Based Decision Support System for UAV Control in a Simulated Environment
by Przemysław Sujecki and Damian Frąszczak
Sensors 2026, 26(8), 2436; https://doi.org/10.3390/s26082436 - 15 Apr 2026
Cited by 1 | Viewed by 356
Abstract
Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS [...] Read more.
Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article
(This article belongs to the Special Issue UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications)
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43 pages, 6083 KB  
Article
An Unscented Kalman Filter Based on the Adams–Bashforth Method with Applications to the State Estimation of Osprey-Type Drones Composed of Tiltable Rotor Mechanisms
by Keigo Watanabe, Soma Takeda and Isaku Nagai
Sensors 2026, 26(6), 2009; https://doi.org/10.3390/s26062009 - 23 Mar 2026
Viewed by 477
Abstract
In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method [...] Read more.
In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone. Full article
(This article belongs to the Special Issue UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications)
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13 pages, 5239 KB  
Article
Transmitarray-Based CATR for Streamline UAV RCS Measurement
by Jiazhi Tang, Run-Kuan Liu, Xiaoming Chen, Guan-Long Huang and Xiuyin Zhang
Sensors 2026, 26(5), 1455; https://doi.org/10.3390/s26051455 - 26 Feb 2026
Viewed by 362
Abstract
Low-altitude unmanned communication platforms play a vital role in integrated sensing and communication (ISAC) systems. Due to the large number of deployed devices and their high level of functional integration, there are high demands for rapid and effective over-the-air testing. However, conventional measurement [...] Read more.
Low-altitude unmanned communication platforms play a vital role in integrated sensing and communication (ISAC) systems. Due to the large number of deployed devices and their high level of functional integration, there are high demands for rapid and effective over-the-air testing. However, conventional measurement systems are limited by structural complexity and size, making them unsuitable for compact, efficient, and mass-production testing. To address these challenges, this paper proposes a rapid radar cross section (RCS) measurement method for streamline measurement of batch low-altitude communication equipment, based on a transmitarray compact antenna test range (TA-CATR). The proposed method performs RCS measurements over elevation and azimuth angles by combining background subtraction, extrapolation, and interpolation techniques, achieving measurable ranges of −45° to 45° and 0° to 360°, respectively. This approach offers a compact, cost-effective, and highly efficient solution for large-scale production testing. Experimental results validate the accuracy and practicality of the proposed TA-CATR-based RCS measurement method. Full article
(This article belongs to the Special Issue UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications)
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23 pages, 3561 KB  
Article
Designing a Drone Control Station for Team Missions with Educational Drones
by Jessika Delgado, Bushra Younas, Jaeho Kim and Sungsoo Ahn
Sensors 2026, 26(4), 1281; https://doi.org/10.3390/s26041281 - 16 Feb 2026
Viewed by 660
Abstract
Educational drones have become increasingly important in education and research due to their affordability, user-friendly design and control, and potential use as tools in STEM (Science, Technology, Engineering, and Math) learning. For example, CoDrone EDUs are used to teach basic programming principles and [...] Read more.
Educational drones have become increasingly important in education and research due to their affordability, user-friendly design and control, and potential use as tools in STEM (Science, Technology, Engineering, and Math) learning. For example, CoDrone EDUs are used to teach basic programming principles and drone control to high school or university students. As drones in real-world applications often collaborate to solve problems, controlling multiple educational drones in a team is crucial and beneficial for enhancing students’ problem-solving and design skills. However, these educational drones primarily rely on one-to-one control via a radio-frequency remote controller, and programming libraries for coordinating multi-drone missions are limited, posing challenges for students or developers in controlling them effectively. To address the lack of control in missions with multiple educational drones, we present a drone control station (DCS), featuring a centralized architecture that connects and controls various drones. We first develop scenarios and use cases that utilize multiple drones, specifying the system requirements. We then design conceptual models and architectures for the DCS. Next, we implement the DCS and evaluate whether it achieves the team missions. Experimental results show that the DCS with the centralized architecture is suitable for team missions with multiple educational drones. We expect the approach in our work to serve as a method for controlling multi-drone missions in an educational environment. Full article
(This article belongs to the Special Issue UAVs as Mobile Sensing Platforms: Advances, Innovations, and Emerging Applications)
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