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Sensors and Measurements for Unmanned Systems
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Sensors and Measurements for Unmanned Systems

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

Deadline for manuscript submissions: 25 November 2024 | Viewed by 5039

Special Issue Editors

Prof. Dr. Pasquale Daponte

E-Mail Website
Collection Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: ADC and DAC modeling and testing; digital signal processing; distributed measurement systems; aerial photogrammetry; unmanned aerial systems (UASs); automatic test equipment for UASs
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Picariello

E-Mail Website
Collection Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: electrical and electronic instrumentation; data acquisition systems (DAQs) based on compressive sampling (CS); biomedical instrumentation; distributed measurement systems, including wireless sensor networks (WSNs); Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Lamonaca

E-Mail Website
Collection Editor
1. Department of Computer Engineering, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, CS, Italy
2. CNR-NANOTEC, 87036 Rende, CS, Italy
Interests: measurements; distributed measurement systems; measurement and monitoring systems based on the IoT; measurement and monitoring systems based on AI; wireless sensor network; synchronization of measurement instruments and sensors; non-invasive measurements; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Technology advances have enabled the development of unmanned systems/vehicles used in the air, on the ground or on/in the water. The application range for these systems is continuously increasing, and unmanned platforms continue to be the subject of numerous studies and research contributions, especially in the measurement and monitoring field. 

This Topical Collection deals with the role of sensors and measurements in ensuring that unmanned systems work properly, meet the requirements of the target application, provide and increase their navigation capabilities and suitably monitor and gain information on several physical quantities in the environment around them. The study of the critical environmental factors affecting unmanned system performance and the development of suitable models for their performance prediction are also topics of this Topical Collection, together with the potentiality that these kinds of vehicles have in measurement fields. 

High-quality research articles as well as reviews are welcome for the following and related fields: new technology for metrology-assisted production in aerospace, naval and terrestrial unmanned vehicle industries, unmanned component measurement, sensors and associated signal conditioning, calibration methods for electronic testing and measurement for unmanned systems and vehicles.

Prof. Dr. Pasquale Daponte
Dr. Francesco Picariello
Prof. Dr. Francesco Lamonaca
Collection 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. 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

  • sensors
  • unmanned systems
  • measurement methods
  • measurement uncertainty
  • calibration
  • navigation
  • sense and avoid
  • image processing
  • signal processing
  • testing methods for unmanned systems
  • electronic instrumentation for unmanned systems
  • automatic test equipment
  • sensors and sensor systems for unmanned systems applications
  • wireless sensor network nodes based on unmanned systems
  • attitude and heading reference systems
  • monitoring systems
  • metrology for navigation and precise positioning
  • sensors and data fusion techniques
  • navigation testing instrumentation and navigation test techniques
  • unmanned systems swarms
  • unmanned systems safety and security
  • aerial, naval and terrestrial photogrammetry and 3D reconstruction of environment
  • health structural monitoring with unmanned systems
  • precision agriculture
  • search and rescue with unmanned systems
  • environmental monitoring with unmanned systems
  • indoor and outdoor navigation systems
  • accurate positioning in forestry and in non-line-of-sight scenarios

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Published Papers (2 papers)

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Research

16 pages, 5277 KiB  
Article
Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters
by Zhuoyao He, David Martín Gómez, Arturo de la Escalera Hueso, Pablo Flores Peña, Xingcai Lu and José María Armingol Moreno
Sensors 2023, 23(14), 6429; https://doi.org/10.3390/s23146429 - 15 Jul 2023
Cited by 3 | Viewed by 1838
Abstract
Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; [...] Read more.
Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data. Full article
(This article belongs to the Special Issue Sensors and Measurements for Unmanned Systems)
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23 pages, 8471 KiB  
Article
The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems
by Giorgio de Alteriis, Davide Ruggiero, Francesco Del Prete, Claudia Conte, Enzo Caputo, Verdiana Bottino, Filippo Carone Fabiani, Domenico Accardo and Rosario Schiano Lo Moriello
Sensors 2023, 23(13), 6127; https://doi.org/10.3390/s23136127 - 3 Jul 2023
Cited by 1 | Viewed by 2111
Abstract
In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the [...] Read more.
In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters. Full article
(This article belongs to the Special Issue Sensors and Measurements for Unmanned Systems)
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