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UAV and Sensor Applications for Navigation and Positioning—2nd Edition
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UAV and Sensor Applications for Navigation and Positioning—2nd Edition

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2239

Special Issue Editors

Dr. Yongbo Zhang

E-Mail Website
Guest Editor
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Interests: sensor applications for navigation and positioning; filtering and information fusion; structural safety and reliability; intelligent aircraft technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuhang Li

E-Mail Website
Guest Editor
Institute of Solid Mechanics, Beihang University (BUAA), Beijing 100191, China
Interests: fabrication of wearable electronics and flexible electronics; thermal management of flexible electronics; mechanic design of flexible electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are widely used, from military to civilian and commercial uses, such as military reconnaissance, power inspection, security inspection, and other special tasks. However, the prerequisite for UAVs to complete tasks is achieving high-precision navigation and positioning, that is, a UAV should know where it is in real time. Global navigation satellite system (GNSS) technology is the mainstream navigation and positioning method for UAVs in open areas, while in GNSS-denied environments, such as dense forests, mountains, and indoors, it becomes a challenge to realize the high-precision navigation and positioning of UAVs.

Benefiting from the development of microelectromechanical systems (MEMS), a variety of sensors can be carried on UAVs, and the navigation and positioning technology based on the fusion of multi-sensor data has become a research hotspot. The vigorous development of cluster technology, machine learning, and simultaneous localization and mapping (SLAM) brings more possibilities for UAV navigation and positioning methods. Researchers are making innovative achievements in these areas, such as SLAM, cooperative navigation and positioning for multi-UAVs, the applications of machine learning, and so on.

This Special Issue aims to solicit innovative papers related to “UAV and Sensor Applications for Navigation and Positioning”, including but not limited to the following areas:

  • Navigation and positioning method in GNSS-denied environments.
  • Multi-sensor data fusion method for UAV navigation and positioning.
  • Cooperative navigation and positioning method for multi-UAV.
  • SLAM.
  • Vision-based navigation and positioning method.
  • Applications of machine learning in UAV navigation and positioning.

Dr. Yongbo Zhang
Prof. Dr. Yuhang Li
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

  • unmanned aerial vehicles
  • sensor applications
  • navigation and positioning
  • SLAM
  • machine learning
  • data fusion

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Related Special Issue

Published Papers (2 papers)

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Research

13 pages, 6985 KB  
Article
UAV-Deployable Open-Source Sensor Nodes for Spatial and Temporal In Situ Water Quality Monitoring and Mapping
by Matthew Burnett, Mohamed Abdelwahab, Joud N. Satme, Austin R. J. Downey, Gabriel Barahona Smith, Antonio Fonce and Jasim Imran
Sensors 2026, 26(4), 1158; https://doi.org/10.3390/s26041158 - 11 Feb 2026
Viewed by 740
Abstract
Cost efficient, spatially resolved water quality monitoring is essential for managing pollution and protecting aquatic ecosystems. This study presents a low-cost (approximately USD 200), open-source, unmanned aerial vehicle (UAV)-deployable in situ sensor node for real-time assessment of surface-water conditions. The system integrates sensors [...] Read more.
Cost efficient, spatially resolved water quality monitoring is essential for managing pollution and protecting aquatic ecosystems. This study presents a low-cost (approximately USD 200), open-source, unmanned aerial vehicle (UAV)-deployable in situ sensor node for real-time assessment of surface-water conditions. The system integrates sensors for pH, turbidity, temperature, and total dissolved solids (TDSs), with onboard data logging and real-time clock (RTC) synchronization. Bench validation of the sensor package yielded mean absolute percentage errors of 1.34% for pH, 5.23% for TDS, and 0.81% for temperature, and the device operated continuously for 42 h. Field deployment demonstrated its ability to resolve spatial gradients, with observed ranges in the tested water body of pH 6.0–6.7, turbidity 11–18 NTU, TDS 44–51 ppm, and temperature 22.8–24.6 °C. Ordinary Kriging was used to interpolate measurements and generate continuous spatial maps. The open-source, UAV-deployable design provides an accessible platform for community-scale and research-oriented water quality mapping. Full article
(This article belongs to the Special Issue UAV and Sensor Applications for Navigation and Positioning—2nd Edition)
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23 pages, 3214 KB  
Article
Enhanced GNSS Navigation Using a Centered Error Entropy Extended Kalman Filter in Non-Gaussian Noise Environments
by Yi Chang, Dah-Jing Jwo and Bo-Yang Lee
Sensors 2026, 26(4), 1148; https://doi.org/10.3390/s26041148 - 10 Feb 2026
Viewed by 442
Abstract
Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or [...] Read more.
Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or outliers. The traditional extended Kalman filter (EKF), based on the minimum mean square error (MMSE) criterion, assumes Gaussian noise distributions and exhibits degraded performance under non-Gaussian conditions. To overcome this limitation, the minimum error entropy (MEE) criterion was proposed to reduce random uncertainty in estimation error distributions; however, due to its translation invariance property, MEE may inadvertently increase bias when errors contain systematic offsets, leading to poor convergence. In contrast, the maximum correntropy criterion (MCC) concentrates the error probability density function (PDF) around zero, enabling effective entropy adjustment even in the presence of bias and achieving superior error convergence. This paper presents the centered error entropy (CEE) extended Kalman filter (CEE-EKF) that integrates the complementary merits of both MEE and MCC approaches to overcome their individual limitations. Experimental validation in complex nonlinear GPS environments with non-Gaussian noise demonstrates that the CEE-EKF significantly outperforms individual algorithms in noise suppression, particularly exhibiting enhanced robustness and accuracy when handling outliers. These results offer an effective approach to enhancing the reliability of GPS navigation in challenging real-world environments, and the algorithm can be readily extended to other GNSS applications. Full article
(This article belongs to the Special Issue UAV and Sensor Applications for Navigation and Positioning—2nd Edition)
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