UAVs Research Articles

Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Evaluating the ability of convolutional neural networks for transfer learning in Pinus radiata cover predictions

The species Pinus radiata is highly invasive in native forests in Chile, drastically affecting the functioning and structure of ecosystems. Hence, it is imperative to develop robust approaches to detect P. radiata invasions at different scales. Models based on convolutional neural networks (CNN) have proven to be a promising alternative to detect plant invasions in high-resolution remote sensing data, such as those obtained by drones. However, studies have been limited in their spatial variability and their assessments of transferability or transfer learning to new sectors, hindering the ability to use these models in a real-world setting. We train models based on CNN architectures using unpiloted aerial vehicle data and evaluate their ability to transfer learning outside the training domain using regression approaches. We compared models trained with low spatial variability (mono-site) with those with high spatial variability (multi-site). We further sought to maximize the transference of learning outside the training domain by searching among different architectures and models, maximizing the evaluation in an independent data set. The results showed that transfer learning is better when multi-site models with higher spatial variability are used for training, obtaining a coefficient of determination R2 between 60% and 87%. On the contrary, mono-site models present a wide variability of performance attributed to the dissimilarity of information between sites, limiting the possibilities of using these models for extrapolations or model generalizations. We also obtained a significant difference between within-domain generalization using test data versus transfer learning outside the training domain, showing that testing data alone cannot depict such discrepancy without further data. Finally, the best models for transfer learning on new data domains often do not agree with those selected by the standard training/validation/testing scheme. Our findings pave the way for deeper discussions and further investigations into the limitations of CNN models when applied to high-resolution imagery.

Towards early forest fire detection and prevention using AI-powered drones and the IoT

Wildfires are a significant natural hazard, resulting in financial losses, human deaths, and environmental damage. Due to the rising severity and frequency of wildfires, wildfire management and detection have recently received increased attention worldwide. Monitoring potential risk areas and early fire detection are critical factors for shortening the reaction time and reducing the potential damage. Conventional wildfire detection techniques like satellite imaging and remote camera-based sensing need more latency and low reliability. To tackle these limitations, this paper proposes a novel airborne UAV-based IoT (UIoT) system for wildfire sensing, detection, and extinguishing. It presents the design of low-cost and low-maintenance fire-detecting IoT nodes for large-scale deployment. It also proposes, investigates, and reports on several connectivity architectures using the LoRaWAN protocol for UIoT systems. We geolocate forest trees in a terrain-mapping exercise for precise fire localization. We then deploy an autonomous drone with a visual camera that utilizes our novel classification network to detect the presence of fire followed by fire detection and particle filter-based tracking to center fire at the center of the frame. We use a cloud back-end server for monitoring, analysis, and reporting. Our results show that our low-cost and long-range IoT nodes accurately detect fire within 1-5 min after fire ignition. Our fire classification network achieved an accuracy of 99.46% and a mean average precision of 99.64%. Numerical results suggest that the proposed UAV-IoT-based fire classification offers a fast and reliable wildfire recognition and extinguishing solution while minimizing power consumption and increasing the battery lifespan.

Landsystem analysis of temperate non‐surging glaciers on the Mýrdalsjökull Ice Cap, southern Iceland

Glacial deposits are important sources of palaeoclimatic information but not all deposits are formed by processes that reflect the overall climatic conditions of a region; surge‐type glaciers undergo periodic episodes of rapid ice movement, often unrelated to ambient climatic conditions. This study examines the glacier forefields of Öldufellsjökull and western Sléttjökull, two outlet glaciers of the Mýrdalsjökull Ice Cap in southern Iceland, to identify landform characteristics indicative of past episodes of fast flow. Previous studies suggest episodes of fast flow at these glaciers in the past century. Remotely sensed data and field investigations were combined to complete a landsystem analysis of the forefields of these glaciers and an uncrewed aerial vehicle was used to collect high‐resolution imagery of areas of particular interest. Two assemblages of landsystems are identified on each forefield, which pass from streamlined landforms containing abundant flutes close to the glacier to spatially restricted bands of arcuate moraines with associated glaciofluvial and glaciolacustrine deposits more distally. This distribution of landsystem tracts has limited similarity to the current surge‐type glacier landsystem model, suggesting that other processes are controlling the development of landform–sediment assemblages. Using a high‐resolution digital elevation model of an area within the 1984 ice margin, two distinct landform types were identified that were not apparent on the coarse resolution imagery: hummocky moraine and a circular feature hypothesized to have formed as a result of water escape caused by changing hydrological regimes. The forefields of Öldufellsjökull and western Sléttjökull lack many of the characteristics typical of surge‐type landsystems and instead are more similar to the active temperate landsystem common in Iceland.

COMPARATION OF PHOTOGRAMMETRY AND TERRESTRIAL LASER SCANNING METHODS FOR EROSION MONITORING IN THE AREA OF DEVIL’S TOWN: PROJECT “DEMONITOR”

<p>Project "Devils’ town Erosion MONITORing - DEMONITOR" involves the monitoring of accessible earth pillars in the area of Devil’s town, by using a combination of several non‐invasive methods. Terrestrial laser scanning (TLS) and photogrammetric imaging with unmanned aircraft (UAV) as a platform showed as great solutions for 3D modeling of this site and erosion monitoring. In this work it is shown that using manual free flight mode for imaging with UAV gave much better results than the missions performed with predefined flight plans. The desired long‐term effect from this research should have a significant part in the overall socioeconomic development of the municipality of Kuršumlija, and the entire Toplica district.</p>

An automated model‐based testing approach for the self‐adaptive behavior of the unmanned aircraft system application software

SummaryThe unmanned aircraft system (UAS) is rapidly gaining popularity in civil and military domains. A UAS consists of an application software that is responsible for defining a UAS mission and its expected behavior. A UAS during its mission experiences changes (or interruptions) that require the unmanned aerial vehicle (UAV) in a UAS to self‐adapt, that is, to adjust both its behavior and position in real‐time, particularly for maintaining formation in the case of a UAS swarm. This adaptation is critical as the UAS operates in an open environment, interacting with humans, buildings, and neighboring UAVs. To verify if a UAS correctly makes an adaptation, it is important to test it. The current industrial practice for testing the self‐adaptive behaviors in UAS is to carry out testing activities manually. This is particularly true for existing UAS rather than newly developed ones. Manual testing is time‐consuming and allows the execution of a limited set of test cases. To address this problem, we propose an automated model‐based approach to test the self‐adaptive behavior of UAS application software. The work is conducted in collaboration with an industrial partner and demonstrated through a case study of UAS swarm formation flight application software. Further, the approach is verified on various self‐adaptive behaviors for three open‐source autopilots (i.e., Ardu‐Copter, Ardu‐Plane, and Quad‐Plane). Using the proposed model‐based testing approach we are able to test sixty unique self‐adaptive behaviors. The testing results show that around 80% of the behavior adaptations are correctly executed by UAS application software.

Experimental Assessment of the Flow Recirculation Effect on the Noise Measurement of a Free-Flying Multi-rotor UAS in a Closed Anechoic Chamber

AbstractThe flow recirculation effect on the noise measurement of a multi-rotor unmanned aircraft system (UAS) hovering in a closed anechoic chamber is experimentally characterized in this work. The measured acoustic spectrogram reveals that the recirculation forms around 30 s after the UAS’s take-off, manifested as prominent fluctuations in blade passage frequency and its harmonics. However, the instantaneous overall sound pressure level shows no obvious increase with the development of the recirculation. The result indicates that the recirculation effect does not significantly change the total acoustic energy but increases the uncertainties in the spectral distribution, which can be quantified by spectral entropy. The quantitative analysis of different noise components shows that the recirculation has a minimal effect on the tonal noise levels but slightly increases the broadband noise level out of the rotors’ plane. The results from parametric tests suggest that this broadband noise increment has a positive correlation with the UAS’s hover height but a negative correlation with the UAS’s gross mass. The comparison with existing studies highlights the difference in the recirculation effect on the noise of isolated rotor(s) and free-flying multi-rotor UAS in confined spaces.

Autonomous robotic drone system for mapping forest interiors

Abstract. During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).

Auction-Based Distributed Task Allocation Algorithm for Drone Swarms

Research on drone swarms has gained traction in recent times due to their ability to outperform single drones in various tasks. In order for autonomous drone swarms to carry out tasks effectively, it is crucial that the tasks are distributed among the drones. This poses a considerable challenge that must be addressed in the advancement of drone swarm technology. The assignment problem is basically defined as the process of assigning M tasks to N drones in a way that minimizes the total cost. This paper presents an auction-based distributed task allocation algorithm called Harmony DTA (Drone Task Allocation) for heterogeneous drone swarms. Prior research primarily focuses on minimizing overall costs associated with assignments. In contrast, Harmony DTA not only minimizes total costs through an enhanced cost calculation function, but also ensures equitable distribution of workload among drones. Additionally, the proposed two-stage auction process reduces the total message size utilized during communication. Matlab simulations and field tests were conducted to assess the effectiveness of the proposed algorithm. Moreover, the algorithm's performance was evaluated in comparison to the CBBA (Consensus Based Bundle Algorithm). The results obtained indicated that the proposed algorithm is capable of assigning tasks with high efficiency, without encountering conflicts.

Interpreting environments of deposition from facies analysis of outcrop versus seismic reflection data: A cautionary tale from the Mount Messenger Formation, Taranaki Basin (New Zealand)

The lower part of the Mount Messenger Formation, which crops out along the North Taranaki coast (North Island, New Zealand), is widely regarded to display classic examples of exhumed basin floor fan strata. This interpretation is supported by numerous publications on the sedimentary facies and depositional environments inferred for these strata, which are exposed in coastal cliff exposures about 20 m high with limited lateral extent. Consequently, for almost three decades, this succession has served as an analogue and training ground for geoscientists investigating the features of basin floor fan deposits and their reservoir characteristics. Here, we bring together seismic stratigraphy and seismic geomorphology of 2D and 3D seismic reflection datasets, as well as photogrammetric models based on images collected using uncrewed aerial vehicle (UAV) drones from selected locations along the outcrop belt to challenge this paradigm. We analyzed seismic reflection data in an area immediately offshore and down-dip of the strata exposed in the coastal outcrop and found no objective criteria for submarine fan depositional elements based on accepted seismic stratigraphic criteria. Furthermore, mapping of seismic reflectors revealed large submarine channel systems (up to 3 km wide and up to 200 m deep) within the lower part of the Mount Messenger Formation. Submarine channel depositional elements (less than 250 m wide and 25 m thick) are also observed to incise through the outcrops, but they are generally below seismic resolution. In the context of the regional geology, we conclude that the lower Mount Messenger Formation accumulated as continental slope sediments cross-cut by channels rather than as basin floor fans. This study issues a cautionary tale about applying facies analysis to outcrop with limited 3D exposure to interpret sedimentary environments. The spatial distribution of facies and architectural elements viewed in seismic data are invaluable for testing the veracity of facies interpretations.

Study of the reflection of a plane electromagnetic wave from a layer of chiral metamaterial located on a conducting plane, taking into account the inhomogeneity and dispersion of material parameters

To reduce the radar signature of moving objects, such as cars, boats, manned and unmanned aircraft systems, technologies for creating broadband radio-absorbing materials are being actively developed and improved. Among such materials are composites and chiral metamaterials, which have unique characteristics that make it possible to effectively control the reflection and rotation of the plane of polarization of electromagnetic waves. In addition, devices included in the structure of infocommunication systems are subject to electromagnetic radiation, which can cause undesirable effects and interference. Radio-absorbing coatings based on metamaterials can significantly reduce the reflection of electromagnetic waves and effectively counteract the effects of electromagnetic radiation. The purpose of this study is to solve the scientific and technical problem of determining the reflection coefficients of electromagnetic waves from a radio-absorbing material consisting of a chiral metamaterial located on a conducting surface. The main objective of the work is to study the interaction of a plane electromagnetic wave with the specified layer of metamaterial located on a conducting plane, taking into account the inhomogeneity and dispersion of material parameters.

Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights

Abstract. The spatiotemporal distribution of aerosol particles in the atmosphere has a great impact on radiative transfer, clouds, and air quality. Modern remote sensing methods, as well as airborne in situ measurements by unpiloted aerial vehicles (UAV) or balloons, are suitable tools to improve our understanding of the role of aerosol particles in the atmosphere. To validate the measurement capabilities of three relatively new measurement systems and to bridge the gaps that are often encountered between remote sensing and in situ observation, as well as to investigate aerosol particles in and above the boundary layer, we conducted two measurement campaigns and collected a comprehensive dataset employing a scanning aerosol lidar, a balloon-borne radiosonde with the Compact Optical Backscatter Aerosol Detector (COBALD), an optical particle counter (OPC) on a UAV, and a comprehensive set of ground-based instruments. The extinction coefficients calculated from near-ground-level aerosol size distributions measured in situ are well correlated with those retrieved from lidar measurements, with a slope of 1.037 ± 0.015 and a Pearson correlation coefficient of 0.878, respectively. Vertical profiles measured by an OPC-N3 on a UAV show similar vertical particle distributions and boundary layer heights to lidar measurements. However, the sensor, OPC-N3, shows a larger variability in the aerosol backscatter coefficient measurements, with a Pearson correlation coefficient of only 0.241. In contrast, the COBALD data from a balloon flight are well correlated with lidar-derived backscatter data from the near-ground level up to the stratosphere, with a slope of 1.063 ± 0.016 and a Pearson correlation coefficient of 0.925, respectively. This consistency between lidar and COBALD data reflects the good data quality of both methods and proves that lidar can provide reliable and spatial distributions of aerosol particles with high spatial and temporal resolutions. This study shows that the scanning lidar has the capability to retrieve backscatter coefficients near the ground level (from 25 to 50 m above ground level) when it conducts horizontal measurement, which is not possible for vertically pointing lidar. These near-ground-level retrievals compare well with ground-level in situ measurements. In addition, in situ measurements on the balloon and UAV validated the scanning lidar retrievals within and above the boundary layer. The scanning aerosol lidar allows us to measure aerosol particle distributions and profiles from the ground level to the stratosphere with an accuracy equal to or better than in situ measurements and with a similar spatial resolution.

Mapping fault geomorphology with drone-based lidar

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.

Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.

Pitot tube measure-aided air velocity and attitude estimation in GNSS denied environment

This paper addresses the problem of estimating air velocity and gravity direction for small autonomous fixed-wing drones in GNSS11Global Navigation Satellite Systems.-denied environments. The proposed solution uses a minimal sensor suite, relying on Pitot tube measurements and Inertial Measurement Unit (IMU) signals, including only gyrometers and accelerometers. The approach combines the Riccati observer and Equivariant Filter designs, using an over-parametrization technique to design an observer on SO(3)×R3 and subsequently re-project to S2×R3 to estimate the gravity direction. The system’s observability is analyzed, and local exponential stability of the origin of the observer error is demonstrated as long as the aircraft attitude is persistently exciting. The observer was evaluated using simulated and real flight data to showcase the estimator’s performance.

Вікові особливості трансформації психофізіологічних функцій операторів безпілотних авіаційних комплексів зі стресогенними захворюваннями органів травлення

The purpose of the study is to identify age-related changes in the psychophysiological parameters of operators of unmanned aircraft systems who have stress-associated diseases of the digestive organs. Research materials and methods. In the course of the conducted research, 58 operators of unmanned aircraft complexes aged from 21 to 50 years old who had stress-related diseases of the digestive organs were examined. The investigated operators were divided into 2 groups based on their age. The examination was carried out using blank questionnaires and modern modified methods implemented on the basis of the «PFI-2» software and hardware complex for psychophysiological research. The results. According to the research data, it was established that the probability of diseases of the digestive organs in operators ˂40 years old is 0.78, and in operators ≥40 years old – 0.22 (p=0.00001). Such a large probability gap can be explained by the fact that the young population is particularly prone to the occurrence and development of diseases of the digestive organs. The comparative analysis also demonstrated the fact that most of the psychophysiological parameters of operators with stress-related diseases worsen with age. Correlation analysis showed that according to speed psychophysiological parameters, there is an unreliable shift in the system of ensuring professional activity with age (p=0.53), аccording to the psychological functions, there is a tendency towards the transformation of changes (p=0.0653), and when studying the structure of cognitive characteristics, it weakens quite a lot (p=0.001). Conclusions. It is shown that most of the listed psychophysiological parameters in operators who have stress-associated diseases reliably worsen with age (p<0.05). It was found that the number of positive and negative correlations in the operators of unmanned aircraft systems aged ≥40 years old is significantly reduced between psychological and cognitive parameters. According to the number of negative correlations between both age groups of operators, their decrease was sufficient between psychological (p=0,014) and cognitive (p=0.05) parameters. That is, in operators who have stress-associated diseases of the digestive organs, the system of connections between individual psychological and cognitive functions weakens with age. A hypothesis was formulated: stress-associated diseases cause an increase in the life support system of the operator, but with age, this system gradually breaks down, unable to withstand the effects of stress loads.

Countering Unmanned Aircraft Systems – A Multi-Domain Effort

Abstract In the last decades, the proliferation of the unmanned aircraft systems (UASs) has altered the modern battlefield and the need to counter these inexpensive, flexible, and expendable systems has become paramount. UASs come in a variety of sizes and capabilities, the larger ones may achieve effects compared to a cruise missile and the smaller systems are difficult to detect as they manoeuvre across the battlefield in order to successfully conduct tactical-level strikes. So it is a must to understand that countering this kind of threat is a shared and joint responsibility and the approach has to be a comprehensive one, covering a broad spectrum of military, civil and legal perspective on this subject matter. Moreover, as it is clear that there is a need to employ multi-domain solutions to nullify the UAS threat, nations should aim to achieve technical interoperability and standardization given by a common doctrine and operating procedures.

Communication Technologies as a Medium for Control and Data Transfer in the Unmanned Aircraft Systems

Abstract The currently available communication technologies used as a medium between the main elements of unmanned aircraft systems – unmanned aerial vehicles and command centers – are discussed. The advantages and disadvantages of different types of communication technologies are analyzed, as well as the relationship between their technical parameters and the operational characteristics of the overall unmanned aerial system. General conclusions have been drawn regarding the correct selection of communication technology as a medium between the main elements of the unmanned aerial system and the tasks it solves.