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Sensors for Aerial Unmanned Systems 2021-2023

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

Deadline for manuscript submissions: closed (24 February 2023) | Viewed by 11871

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Special Issue Editor

Prof. Dr. Carlo Bettanini

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Guest Editor

Department of Industrial Engineering, Via Venezia 1, 35131 Padova, Italy Center for Studies and Activities for Space, Via Venezia 15, 35131 Padova, Italy
Interests: Design, realization, and qualification of instruments and mechanisms for space applications; Design and qualification of Earth low- and high-altitude autonomous flight systems; Methods for prediction and reconstruction of trajectory and attitude of both space and atmospheric flying systems
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Special Issue Information

Dear Colleagues,

On behalf of the Editorial Board of Sensors, it is my honor to inform you about our upcoming Special Issue on Sensors for Aerial Unmanned Systems. We invite you to join us in our efforts to extend and share the latest advancements in sensors for UAVs. Let us know if you can submit a manuscript by the end of July, or inform us of a feasible timeline for your submission. I look forward to your favorable response.

Potential topics include but are not limited to the following:

Sensors for drones and balloon navigation;
Sensors and control for UAV formation flight;
Vision systems and RGB-D sensors for navigation;
Optical sensors for remote sensing, including aerial-based measurement for cultural heritage, aerial-based measurement for precision farming, and aerial-based measurement for geology;
Aerial systems and sensors for light pollution measurement;
Aerial systems and sensors for air pollution measurement;
UAV application in civil engineering and oil and gas industry;
UAV application for planetary exploration;
Sensors for human–UAV interaction;
Machine learning in UAV sensing;
Sensors and algorithms for safe landing;
Active safety devices for UAVs (parachutes and commanded wings).

Prof. Dr. Carlo Bettanini
Guest Editor

Manuscript Submission Information

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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.

Published Papers (6 papers)

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Research

24 pages, 3544 KiB

Open AccessArticle

Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration

by Ahmed Ibrahim, Ashraf Abosekeen, Ahmed Azouz and Aboelmagd Noureldin

Sensors 2023, 23(13), 6097; https://doi.org/10.3390/s23136097 - 02 Jul 2023

Cited by 5 | Viewed by 1746

Abstract

High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle’s onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems’ drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from

19.4

m to

3.3

m with

82.98 %

improvement and the 2D-position max error reduced from

73.9

m to

14.2

m with

80.78 %

improvement. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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26 pages, 17184 KiB

Open AccessArticle

Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region

by Sujan Sapkota and Dev Raj Paudyal

Sensors 2023, 23(12), 5432; https://doi.org/10.3390/s23125432 - 08 Jun 2023

Cited by 5 | Viewed by 2033

Abstract

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green–Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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15 pages, 2524 KiB

Open AccessArticle

The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations

by Lukas Prey, Ludwig Ramgraber, Johannes Seidl-Schulz, Anja Hanemann and Patrick Ole Noack

Sensors 2023, 23(8), 4177; https://doi.org/10.3390/s23084177 - 21 Apr 2023

Viewed by 1697

Abstract

Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R² = 0.27–0.81), but R²-values for the best across-trials models were lower only by 0.03–0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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18 pages, 2016 KiB

Open AccessArticle

Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging

by Jaafar Abdulridha, An Min, Matthew N. Rouse, Shahryar Kianian, Volkan Isler and Ce Yang

Sensors 2023, 23(8), 4154; https://doi.org/10.3390/s23084154 - 21 Apr 2023

Cited by 3 | Viewed by 2707

Abstract

Detecting plant disease severity could help growers and researchers study how the disease impacts cereal crops to make timely decisions. Advanced technology is needed to protect cereals that feed the increasing population using fewer chemicals; this may lead to reduced labor usage and cost in the field. Accurate detection of wheat stem rust, an emerging threat to wheat production, could inform growers to make management decisions and assist plant breeders in making line selections. A hyperspectral camera mounted on an unmanned aerial vehicle (UAV) was utilized in this study to evaluate the severity of wheat stem rust disease in a disease trial containing 960 plots. Quadratic discriminant analysis (QDA) and random forest classifier (RFC), decision tree classification, and support vector machine (SVM) were applied to select the wavelengths and spectral vegetation indices (SVIs). The trial plots were divided into four levels based on ground truth disease severities: class 0 (healthy, severity 0), class 1 (mildly diseased, severity 1–15), class 2 (moderately diseased, severity 16–34), and class 3 (severely diseased, highest severity observed). The RFC method achieved the highest overall classification accuracy (85%). For the spectral vegetation indices (SVIs), the highest classification rate was recorded by RFC, and the accuracy was 76%. The Green NDVI (GNDVI), Photochemical Reflectance Index (PRI), Red-Edge Vegetation Stress Index (RVS1), and Chlorophyll Green (Chl green) were selected from 14 SVIs. In addition, binary classification of mildly diseased vs. non-diseased was also conducted using the classifiers and achieved 88% classification accuracy. This highlighted that hyperspectral imaging was sensitive enough to discriminate between low levels of stem rust disease vs. no disease. The results of this study demonstrated that drone hyperspectral imaging can discriminate stem rust disease levels so that breeders can select disease-resistant varieties more efficiently. The detection of low disease severity capability of drone hyperspectral imaging can help farmers identify early disease outbreaks and enable more timely management of their fields. Based on this study, it is also possible to build a new inexpensive multispectral sensor to diagnose wheat stem rust disease accurately. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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25 pages, 8825 KiB

Open AccessArticle

Deep Learning Based Vehicle Detection on Real and Synthetic Aerial Images: Training Data Composition and Statistical Influence Analysis

by Michael Krump and Peter Stütz

Sensors 2023, 23(7), 3769; https://doi.org/10.3390/s23073769 - 06 Apr 2023

Cited by 3 | Viewed by 1774

Abstract

The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environments for generating synthetic data are increasingly sought. In this article, the complete process chain is evaluated regarding the use of synthetic data based on vehicle detection. Among other things, content-equivalent real and synthetic aerial images are used in the process. This includes, in the first step, the learning of models with different training data configurations and the evaluation of the resulting detection performance. Subsequently, a statistical evaluation procedure based on a classification chain with image descriptors as features is used to identify important influencing factors in this respect. The resulting findings are finally incorporated into the synthetic training data generation and in the last step, it is investigated to what extent an increase of the detection performance is possible. The overall objective of the experiments is to derive design guidelines for the generation and use of synthetic data. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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16 pages, 532 KiB

Open AccessArticle

Impact of Motion-Dependent Errors on the Accuracy of an Unaided Strapdown Inertial Navigation System

by Krystian Borodacz and Cezary Szczepański

Sensors 2023, 23(7), 3528; https://doi.org/10.3390/s23073528 - 28 Mar 2023

Cited by 1 | Viewed by 1070

Abstract

The selection of an appropriate measurement system for an inertial navigation system requires an analysis of the impact of sensor errors on the position and orientation determination accuracy to ensure that the selected solution is cost-effective and complies with the requirements. In the current literature, this problem is solved based on the navigation duration only by considering the time-dependent errors due to sensor bias and random walk parameters or by conducting numerous simulations. In the former case, oversimplifying the analysis will not allow accurate values to be determined, while the latter method does not provide direct insight into the emerging dependencies. In contrast, this article introduces an analytic approach with a detailed model. This article presents general formulas, also written in detail for the measurement system model adopted and various manoeuvres. Although general equations are complicated, the use of piecewise constant motion variables allow us to discern fragments of equations corresponding to individual error sources. The results confirm the effect the carouseling has on the reduction of navigation errors. The general formulas presented extend the potential to analyse the influence of the entire host vehicle motion, while the detailed formulas make dependencies between motion and navigational errors evident. Full article

(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)

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