Search Results (13)

Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article

Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue. Traditional methods, such as modal analysis, often struggle to handle the multivariate complexity of operational conditions and data variability. Recently, deep learning has emerged as a promising alternative to overcome these limitations. However, deep learning models typically operate in a unidirectional manner, where feedback to the inputs is often neglected. In contrast, biological neurons utilize feedback mechanisms to refine and adapt their responses in natural ecosystems, enabling adaptive learning and error correction. In this context, this study proposes an innovative Convolutional Neural Network with Reversed Mapping (CNN-RM) approach to SHM, which incorporates feedback loops and self-correcting mechanisms. Before feeding the data into CNN-RM, the dataset complexity is reduced through time-series-to-images Continuous Wavelet Transform (CWT), followed by a denoising CNN (DnCNN) to mitigate complex behavior under various conditions. For application, this study utilizes a massive dataset collected from multivariate sensors installed on a decommissioned military training aircraft previously used by the British Royal Air Force and now housed in a laboratory environment. The results revealed that the overall mean of classification metrics for the CNN is 0.9673 (training) and 0.9422 (testing), while for CNN-MR, it is 0.9764 (training) and 0.9515 (testing), showing an improvement of 0.94% in training and 1.00% in testing. These results highlight significant advancements in SHM, recommending the consideration of such learning mechanisms in neural learning models. Full article

Air Traffic Services play a crucial role in the safety, security, and efficiency of air transportation. The International Civil Aviation Organization (ICAO) performance-based surveillance concept requires monitoring the actual performance of the surveillance systems underpinning these services. This assessment is usually based on the analysis of data gathered during the normal operation of the surveillance systems, also known as opportunity traffic. Processing opportunity traffic requires data association to identify and assign the sensor detections to a flight. Current techniques for association require expert knowledge of the flight dynamics of the target aircraft and have issues with high-manoeuvrability targets like military aircraft and Unmanned Aircraft (UA). This paper addresses the data association problem through the use of the Multi-Sensor Intelligent Data Association (M-SIOTA) algorithm based on Deep Neural Networks (DNNs). This is an innovative perspective on the data association of multi-sensor surveillance through the lens of machine learning. This approach enables data processing without assuming any dynamics model, so it is applicable to any aircraft class or airspace structure. The proposed algorithm is trained and validated using several surveillance datasets corresponding to various phases of flight and surveillance sensor mixes. Results show improvements in association performance in the different scenarios. Full article

Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%. Full article

Aircraft detection in remote sensing images is an important branch of target detection due to the military value of aircraft. However, the diverse categories of aircraft and the intricate background of remote sensing images often lead to insufficient detection accuracy. Here, we present the CNTR-YOLO algorithm based on YOLOv5 as a solution to this issue. The CNTR-YOLO algorithm improves detection accuracy through three primary strategies. (1) We deploy DenseNet in the backbone to address the vanishing gradient problem during training and enhance the extraction of fundamental information. (2) The CBAM attention mechanism is integrated into the neck to minimize background noise interference. (3) The C3CNTR module is designed based on ConvNext and Transformer to clarify the target’s position in the feature map from both local and global perspectives. This module is applied before the prediction head to optimize the accuracy of prediction results. Our proposed algorithm is validated on the MAR20 and DOTA datasets. The results on the MAR20 dataset show that the mean average precision (mAP) of CNTR-YOLO reached 70.1%, which is a 3.3% improvement compared with YOLOv5l. On the DOTA dataset, the results indicate that the mAP of CNTR-YOLO reached 63.7%, which is 2.5% higher than YOLOv5l. Full article

The quadrotor aircraft has the characteristics of simple structure, high attitude maintenance performance and strong maneuverability, and is widely used in air surveillance, post−disaster search and rescue, target tracking and military industry. In this paper, a robust control scheme based on linear active disturbance rejection is proposed to solve the problem that the quadrotor is susceptible to various disturbances during the take−off process of non−horizontal planes and strong disturbances. Linear Active Disturbance Rejection Control (LADRC) is a product of a tracking differentiator (TD), a linear extended state observer (LESO) and an error feedback control law (PD) and is a control technique for estimating compensation for uncertainty. Radial Basis Function Neural Networks (RBFNN) is a well−performing forward network with best approximation, simple training, fast learning convergence and the ability to overcome local minima problems. Combined with the advantages and disadvantages of LADRC, Adaptive Control and Neural Network, the coupling force between each channel, gust crosswind disturbance and additional resistance of offshore platform jitter in the flight state of the quadrotor are optimized. In the control, the RBF neural network is designed, the nonlinear control signal is wirelessly approximated and the uncertain disturbance to the quadrotor is identified online. Finally, the real−time estimation and compensation are performed by LESO to realize the full−attitude take−off of the quadrotor. In addition, this paper uses adaptive control to optimize the parameters of LADRC to reduce the problem of many LADRC parameters and difficulty to integrate. Finally, the robust control system mentioned in this paper is simulated and verified, and the simulation results show that the control scheme has the advantages of simple parameter adjustment and stronger robustness. Full article

Repeated use of aqueous firefighting foams at military aircraft training centers has contaminated groundwater with per and polyfluorinated alkyl substances (PFAS). To delineate the extent of PFAS contamination, numerous site investigations have occurred, which have generated large quantities of investigation-derived wastes (IDW). The commonly used treatment of incinerating PFAS-tainted IDW is costly, and was recently suspended by the Department of Defense. Given long-term IDW storage in warehouses is not sustainable, our objective was to use electrochemical oxidation to degrade PFAS in contaminated water and then scale the technology toward IDW treatment. This was accomplished by conducting a series of laboratory and pilot-scale experiments that electrochemically oxidized PFAS using direct current with boron-doped diamond (BDD) electrodes. To improve destruction efficiency, and understand factors influencing degradation rates, we quantified the treatment effects of current density, pH, electrolyte and PFAS chain length. By using ¹⁴C-labeled perfluorooctanoic acid (PFOA) and tracking temporal changes in both ¹⁴C-activity and fluoride concentrations, we showed that oxidation of the carboxylic head (-¹⁴COOH → ¹⁴CO₂) was possible and up to 60% of the bonded fluorine was released into solution. We also reported the efficacy of a low-cost, 3D printed, four-electrode BDD reactor that was used to treat 189 L of PFOA and PFOS-contaminated water (C_o ≤ 10 µg L⁻¹). Temporal monitoring of PFAS with LC/MS/MS in this pilot study showed that PFOS concentrations decreased from 9.62 µg L⁻¹ to non-detectable (<0.05 µg L⁻¹) while PFOA dropped from a concentration of 8.16 to 0.114 µg L⁻¹. Efforts to improve reaction kinetics are ongoing, but current laboratory and pilot-scale results support electrochemical oxidation with BDD electrodes as a potential treatment for PFAS-tainted IDW. Full article

This research aimed to analyze the body composition (BC) of different groups of women aircrew units in the Spanish Air Forces for a better understanding and improvement of their operability. Specifically, 184 female aircrew members were analyzed and classified into specialties (38 airplane pilots, age: 32.8 ± 10.8; 26 helicopter pilots, age: 32.0 ± 9.18; and 120 transport aircrew, age: 36.9 ± 8.18). The women’s BC was analyzed with an InBody720 bioimpedance device previously used in the military population. There were differences in the BC among specialties, although there were similarities between airplane and helicopter pilots. Airplane (24.0% ± 10.4%) and helicopter pilots (22.6 ± 6.32%) had a smaller percentage of body fat mass than transport aircrew (26.3 ± 7.51%), but there was uniformity among groups in skeletal muscle mass and soft lean mass. We found no differences in body water among specialties. Differences in BCs were previously reported for men in the air force, and these results in women showed similarities for different job entry requirements, different training needs, and different occupational behaviors among units in the Air Force. These results help to deepen the previous knowledge of women’s BC standards in military units. Although pilots are primarily responsible for the aircraft, healthy habits should be encouraged to keep fit and improve the performance of all aircrew members both in flight and when they are deployed. Full article

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance. Full article

Military operations may result in noise impacts on surrounding communities and wildlife. A recent transition to more powerful military aircraft and a national consolidation of training operations to Whidbey Island, WA, USA, provided a unique opportunity to measure and assess both in-air and underwater noise associated with military aircraft. In-air noise levels (110 ± 4 dB re 20 µPa rms and 107 ± 5 dBA) exceeded known thresholds of behavioral and physiological impacts for humans, as well as terrestrial birds and mammals. Importantly, we demonstrate that the number and cumulative duration of daily overflights exceed those in a majority of studies that have evaluated impacts of noise from military aircraft worldwide. Using a hydrophone deployed near one runway, we also detected sound signatures of aircraft at a depth of 30 m below the sea surface, with noise levels (134 ± 3 dB re 1 µPa rms) exceeding thresholds known to trigger behavioral changes in fish, seabirds, and marine mammals, including Endangered Southern Resident killer whales. Our study highlights challenges and problems in evaluating the implications of increased noise pollution from military operations, and knowledge gaps that should be prioritized with respect to understanding impacts on people and sensitive wildlife. Full article

Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. This paper investigated use of eye gaze trackers in military aviation environment to automatically estimate pilot’s cognitive load from ocular parameters. In the first study, we used a fixed base variable stability flight simulator with longitudinal tracking task and collected data from 14 military pilots. In a second study, we undertook four test flights with BAES Hawk Trainer and Jaguar aircrafts doing air to ground attack training missions and constant G level turn maneuvers up to +5G. Our study found that ocular parameters like rate of fixation is significantly different in different flying conditions. It also significantly correlated with rate of descent during air to ground dive training task, normal load factor (G) of the aircraft during constant G level turn maneuvers and pilot’s control inceptor and tracking error in simulation tasks. Results from our studies can be used for real time estimation of pilots’ cognitive load, providing suitable warnings and alerts to the pilot in cockpit and training of military pilots on cognitive load management during operational missions. Full article

A recent study proposed a systematic “(budgeting) knowledge discovery educational framework” (BKDEF). This BKDEF is focused on guiding staff training courses for enhancing the ability to allocate the “large but limited” budget for single, high-cost product design. However, except for its initial application to support the budget planning for the next generation fighter design, the framework’s effectiveness is still awaiting further scrutiny. This study fills the gap by providing the “second application” of BKDEF, which is to support another similar decision for designing the medium-altitude long-endurance unmanned aerial system (MALE UAS). This paper verified the effectiveness of the framework through an empirical application and obtained the knowledge required to allocate a budget for MALE UAS design following the group-opinion basis. In addition, the original analytical style for the last “decision analysis” phase of BKDEF, which included pure quantitative analytical items in order to understand the similarities and diversities in the individual opinions, was replaced by a comparative study to discover the homogeneity and heterogeneity between the two budgeting decisions in a larger scope. As a consequence, the two criteria sets did not overlap despite both decisions being related to military aircraft design. The absolute weights for the MALE UAS design criteria were more balanced than those for the air-superior fighter design, even if the size of the criteria set was larger. The results pave a way for future studies on how other military aircrafts are designed, as more confidence about the use of a BKDEF can be gained from increasing applications, thus more insightful aerospace knowledge can be exploited in comparisons with these works. Full article

In recent years, a growing emphasis on safety has driven various industries, both in manufacturing and service, to implement a Safety Management System (SMS) in their organisations. SMSs have also been widely implemented in aviation due to both regulatory requirements and voluntary implementation with the aim of decreasing incidents and accidents whilst reducing inefficiencies and costs stemming from the repercussions of safety failures. The aviation industry involves various players for the provision of services ranging from airline operations, maintenance, aerodrome operations, air traffic services, aircraft and component design, manufacturing, and training. Not all organisations in the aviation industry have implemented SMSs. Furthermore, SMS is currently not regulated for all aviation organisations. Whilst technology has played a key role in driving down the number of accidents and incidents in aviation, the growth in air traffic demands having programs in place to further drive down accident rates. In this context, this article provides an investigation to the regulatory framework for the implementation of SMSs in aviation, including the requirements stipulated by the International Civil Aviation Organisation (ICAO) and the status of SMS regulation of key National Aviation Authorities (NAA) and Military Aviation Authorities (MAA), with a focus on organisations involved in airworthiness including initial and continuing airworthiness. This article also investigates the challenges of implementing SMSs in organisations involved in Airworthiness, as well as the benefits that could be gained by service providers as well as NAA’s or MAA’s through SMSs. Full article