Abstract. Traditional PID controllers remain widely used in embedded flight control and stabilization systems. However, in small aircraft Attitude and Heading Reference Systems (AHRS), classical PID approaches are insufficient under sensor noise, drift, vibration, and energy limitations typical for lightweight avionics platforms.
This paper proposes a context-aware, risk-sensitive PID framework for AHRS modernization. The controller integrates sensor reliability estimation, energy-aware modulation, and multi-objective optimization into the PID decision logic. The method reduces oscillatory corrections caused by gyroscope and accelerometer noise while preserving attitude tracking accuracy. Simulation results demonstrate reduced integrated control energy, lower multi-objective cost, improved stability, and enhanced robustness under sensor uncertainty typical for MEMS-based AHRS systems. The proposed structure transforms PID from a purely error-compensation mechanism into an intelligent stabilization module suitable for modern small aircraft avionics.
Keywords: PID controller, AHRS modernization, small aircraft stabilization, contextual control, risk-aware control, energy-efficient avionics, sensor reliability, multi-objective optimization.
Abstract. This paper considers the problem of automated assessment of atmospheric transparency in urban conditions based on images obtained from an unmanned aerial vehicle, using computer vision and deep learning methods. The study explores an approach focused on analyzing visual signs of smoke near the horizon, where the concentration of aerosol pollution is usually most pronounced. For the experimental study, a specialized dataset was formed, including aerial photographs of the urban atmosphere of Almaty taken in January-February 2024, followed by spatial division of the images into nine sectors and manual visual assessment of the transparency level on a discrete scale. This method of marking allowed us to record the spatial heterogeneity of pollution within a single frame and take into account the differences between the sky background, the horizon line, and urban development. Based on the pre-trained MobileNetV2 architecture, two model variants were implemented — classification and regression — which made it possible to compare discrete and continuous approaches to the interpretation of visual information. A comparative analysis showed that the classifier provides higher accuracy of strict class matching (83.9%), while the regression model, when rounding predictions to whole values, demonstrates higher accuracy within a tolerance of ±1 class (97.2%) and a lower level of systematic errors. The results confirm the promise of using UAVs in combination with computer vision methods for local monitoring of atmospheric transparency and highlight the potential of this approach as a supplement to traditional ground-based environmental monitoring systems in urban environments, which is particularly relevant given the limited density of stationary stations.
Keywords: air quality monitoring, UAV data; computer vision; atmospheric transparency, smog; deep learning.
Abstract. The continuous growth of unstructured textual data volumes in aircraft maintenance systems creates a demand for automated analysis methods. Traditional defect categorization using standard codes is often insufficiently detailed to reveal the true root causes of failures, as routine operations and critical malfunctions are frequently combined within a single system category. The subject of this study is the semantic structure of textual fault descriptions related to the exterior lighting system (ATA 33–40) of an aircraft fleet of a single model. The objective of the study is to develop and validate a method for the automatic identification of latent operational patterns and failure modes without the use of labeled data. The methodological foundation of the research is probabilistic topic modeling based on the Latent Dirichlet Allocation (LDA) algorithm. To improve model quality, a specialized text preprocessing procedure was implemented, including the expansion of industry-specific abbreviations and the removal of contextual noise. The optimal model configuration was determined through quantitative analysis of the topic coherence metric (Cv) and an assessment of topic semantic stability. Experimental results show that a six-topic model provides the highest level of interpretability. Analysis of the resulting clusters made it possible to identify design-related defect occurrence zones and to classify failures according to their manifestation type. Latent subgroups corresponding to electrical circuit failures and mechanical damage to structural components were automatically identified. The proposed approach enables the transformation of unstructured maintenance personnel records into detailed diagnostic information, thereby creating opportunities to improve maintenance programs and to transition toward predictive reliability management of specific aircraft subsystems.
Keywords: aircraft, maintenance, external lighting, textual descriptions, natural language processing, topic modeling, Latent Dirichlet Allocation.
Absract. In the context of increasing air traffic intensity and the growing proportion of night and long-haul flights, the management of crew fatigue is becoming a critically important factor in ensuring flight safety. Crew fatigue is recognized as a significant factor affecting the reliability of flight operations and decision-making processes. At the same time, traditional working time limitations do not provide a comprehensive assessment of fatigue-related risks, which necessitates the implementation of integrated approaches. Modern methods, including heart rate variability (HRV) analysis and simulation modeling, enable a more accurate assessment of the functional state of crew members and facilitate the identification of fatigue formation patterns. The research problem lies in the limitations of the prescriptive approach based on Flight Time Limitations (FTL), which does not account for physiological and circadian characteristics of humans. The aim of this study is to develop recommendations for assessing crew fatigue levels in airlines of the Republic of Kazakhstan based on an integrated model consistent with international standards of ICAO, IATA, and EASA. The objectives include the analysis of international fatigue management approaches, the development of a mathematical model of the fatigue index, its statistical validation, and the formulation of practical recommendations. The research methods include multiple and logistic regression, ROC analysis, calculation of sensitivity and specificity, Monte Carlo simulation, and heart rate variability (HRV) analysis. The results demonstrated the superiority of the integrated FRMS model (AUC = 0.623) compared to the prescriptive FTL-based model (AUC = 0.574). Simulation modeling showed a reduction in fatigue risk by up to 17% under optimized scheduling conditions. A statistically significant relationship between fatigue, night workload, and decreased HRV was identified. It is concluded that the implementation of an integrated fatigue index within the safety management systems of airlines in the Republic of Kazakhstan is justified. The proposed recommendations enable a transition to proactive fatigue risk management and are consistent with international standards.
Keywords: crew fatigue, fatigue risk management, safety management system, working time limitations, heart rate variability, biomathematical modeling analysis, aviation safety.
Abstract. This article examines approaches to improving the reliability and resilience of information exchange for unmanned systems, taking into account the presence of external interference. The primary issue addressed in this article is the analysis of natural and artificial factors that may affect the operation of data transmission channels. Emphasis is also placed on the impact of artificial electromagnetic disturbances. The paper’s scientific innovation is that it considers the unmanned system as a multi-agent environment. The agents are the operator, the aircraft, and the external environment. The goal of the study is to improve the reliability of the data exchange channel by implementing Visible Light Communication technology. To achieve this goal, the authors formalized quantitative control assessment methods, developed a structural and functional model of the control system, and conducted simulation tests to assess risks. An analysis of experimental data and computer modeling demonstrated that Visible Light Communication, operating in the infrared range, provides a stable information exchange channel over a distance of up to 3 kilometers. This result demonstrates the potential of using this technology to build a noise-resistant and secure information exchange channel for unmanned systems.
Keywords: Unmanned aerial vehicle system, Visible Light Communication technology, risk assessment, simulation model, control system model.
Abstract. This work presents a mathematical model for determining the coordinates of integrated groups of unmanned aerial vehicles (UAVs). The main application areas of UAVs and promising directions for the development of unmanned aviation systems, integrating complexes of remotely controlled vehicles, are analyzed. To improve the stability and reliability of interaction in such systems, stable structures of integrated groups are proposed. Special attention is given to methods for modeling integrated UAV groups, including mathematical description and computer simulation. The structures of integrated groups are analyzed, taking into account the criteria of topological stability during movement and the reliability of data transmission channels based on visible light communication (VLC) technologies. To obtain results, mathematical descriptions and corresponding computer models of integrated UAV groups were developed, meeting the criteria of topological stability and reliability of data transmission channels. Modeling was performed in Matlab R2020b, where computer models of UAV swarms were constructed in a three-dimensional coordinate system.
Additionally, the work considers different types of topologies for integrated UAV groups and their impact on the stability and reliability of system performance.
Keywords: method, group of unmanned aerial vehicles, control, model, data transmission channels, artificial intelligence.
Abstract. This paper explores modern techniques to process and enhance the reliability of radar signals amid strong noise and interference typical for the secondary surveillance radar (SSR) band at 1030/1090 MHz. Air-traffic radar systems play a critical role in ensuring safe and efficient airspace management, however, their performance degrades under strong noise and interference typical of the 1030/1090 MHz band. A comprehensive analysis of filtering and adaptive signal processing algorithms was conducted to improve the signal-to-noise ratio, stabilize target response parameters, and lower false detection rates. For objective comparison, both traditional and advanced digital filtering methods were evaluated. Special focus was placed on comparing the Median, Butterworth, and recursive Kalman filters, implemented and tested in MATLAB with synthetic data and simulated radar scenarios. The effects of filter order, bandwidth, adaptation coefficients, and sampling interval on signal reconstruction quality were investigated. Using statistical, correlation, and probabilistic analyses, an enhanced adaptive threshold detection method was developed, which accounts for the non-stationary background noise, temporal signal correlations, amplitude fluctuations, and dynamic interference parameters in real time. Results demonstrate that combining and recursively applying these filters greatly enhances the robustness of secondary radars against random and systematic interference, reduces estimation error variance, and improves radar reliability. The practical significance lies in the potential integration of these methods into intelligent air-traffic management systems, which can enhance interference immunity, aircraft identification accuracy, and overall radar surveillance quality in civil aviation.
Keywords: secondary radar, adaptive filtering, Kalman filter, Butterworth filter, median processing, digital signal processing, signal interference, reliability of radar surveillance.
Abstract. The aim of the study is to study the impact of data quality, the factors associated with it, and to formalize the logical and mathematical connections between the arguments of the neural network training function or artificial intelligence to demonstrate the relationship with flight safety.
To achieve the goal, it is necessary to solve the following tasks. The first is to assess the current state of the issue of the use of artificial intelligence in the procedures of pre-flight inspection, as well as repair and maintenance of the aircraft. This is necessary to determine the level of technological integration of neural networks and artificial intelligence. Secondly, based on the data obtained, it is necessary to determine relevant platforms for training artificial intelligence. This will allow you to identify additional technical arguments that affect the final result of AI training. The third is to formalize the logical and mathematical connection between the influencing factors and the final result. Identify additional influencing factors and also formalize. The formal recording method allows for the construction of a procedurally consistent communication line to monitor safety risks.
The following methods were used to solve the problems. An observation method that has been applied to the information that has been collected in the course of tracking the history of the application of various automated optical fault detection technologies. Decomposition, which made it possible to separate the functional part of the computer program that detects malfunctions from the complex technology. Comparative analysis, which made it possible to determine the strengths and weaknesses of various neural networks and the architectures of technical systems for these neural networks and artificial intelligence Mathematical analysis, which allows you to formalize the expressions that characterize the influence of the arguments of a complex function and determine the additivity and multiplication of a complex function. Sabotage and functional analyses, which make it possible to determine the relationship between the arguments of complex functions and the final complex safety function.
As a result, expressions were presented that reflect the logical and mathematical connection in a functionally sequential transfer line from the arguments of a neural network and artificial intelligence to a complex indicator of flight safety.
Keywords: artificial intelligence, neural networks, pre-flight inspection, artificial intelligence models, comparative analysis, flight safety.
Annotation. This article explores the perspective opportunities for securely broadcasting the pulse signals emitted by the ADS-B (Automatic Dependent Surveillance–Broadcast) system, which is currently used in modern aviation systems, against external cyber threats. The objective is to enhance the security level of the existing infrastructure, optimize frequency assessment, and improve traffic management through predictive modeling, thereby enabling more efficient and proactive control. The proposed integration architecture employs deep learning algorithms to analyze aircraft signals and provides functionalities such as signal congestion management, real-time risk forecasting, and proactive prediction of weather and traffic changes. Furthermore, the article presents a performance evaluation of the system operating at 1090 MHz and 978 MHz frequencies and proposes methods for frequency optimization. Research results indicate that incorporating the recognition of device identification via radiometric fingerprints into the ADS-B platform not only enhances security and operational efficiency but also significantly improves the system’s adaptability and responsiveness. This approach opens new avenues for the development of smarter and more predictable future aviation networks.
Keywords: ADS-B technology, aviation safety, frequency optimization, real-time data processing, air traffic control.
Abstract. Noise pollution has become a primary concern, as it often disrupts a person’s activities or lifestyle balance. The work is devoted to the analysis of the regulatory framework in the field of the introduction of standards for the maximum permissible levels of sound and ultrasonic signals. The relevance of the work is related to the lack of a comprehensive study of the issues of regulatory support for the measurement of ultrasonic signals, as well as the procedure for identifying sources of ultrasound dangerous to human health. The subject of the study is the regulatory framework and standards for measuring the levels of sound and ultrasonic signals. Ultrasound processing is attracting more and more attention from people, as ultrasound technology can provide a flexible “green” alternative for energy-efficient processes, but powerful sources of ultrasound are harmful to human health. They are especially dangerous because a person may not know or feel about the effects of ultrasound sources on him. The objectives of the study were to identify differences in acceptable signal levels and the measurement features of these levels. The purpose of the work is to determine the completeness of compliance of Kazakhstani regulatory documents with the International Standard. The maximum permissible standards of sound and ultrasonic pressure, legally established in a number of countries, are given. Millions of people around the world are exposed to potentially dangerous levels of noise, and therefore there is an urgent global need for legislation to adequately protect workers’ hearing health. Attention is drawn to the wide variation in the levels of established norms and the reasons leading to the ambiguity of these values. The fact that there are no regulatory and methodological documents on the identification of sources of powerful ultrasonic signals, whose increased danger lies in their inaudibility to humans, is noted. It is concluded that it is necessary to study ultrasound levels in the cabin and cabin of jet aircraft.
Keywords: acoustics, sound, sound pressure, norm, danger, airplane, standard, ultrasound.
Abstract. This article presents the development of an anti-drone system based on artificial intelligence (AI) for detection, radio-frequency jamming, and destruction of unmanned aerial vehicles (UAVs). Drawing on the analysis of UAV use in modern conflicts (Syria, Ukraine) and national regulations of the Republic of Kazakhstan, system requirements were defined, taking into account real sensor parameters (X-band radar with RCS 0.01 m², IR sensor sensitivity 0.1 K), jamming power (50 W in the 400–6000 MHz range), operational conditions (+50 °C, dust storms), and external factors (weather reducing probabilities by 20%). A probabilistic model with AI coordination (neuro-symbolic approach) was proposed, providing novelty through adaptation to Central Asian conditions. The simulation, implemented using Monte Carlo with 1000 iterations, is published in an open GitHub repository with replication instructions. Modeling shows validated results: detection probability of 95.8%, jamming effectiveness of 54–78%, and destruction probability of 70.7–84.6% for guided and autonomous drones at distances of 5–8 km. Comparative analysis with analogs (“Drone Dome”, “Pantsir-S1”) demonstrates superiority in range (10 km vs. 3.5 km), cost efficiency, and adaptability. Computational complexity analysis (O (1) per drone) and optimization pathways (ML for trajectory prediction, distributed data processing) confirm practical applicability. The system is expected to strengthen Kazakhstan’s defense capacity and reduce dependency on foreign technologies.
Keywords: anti-drone complex, artificial intelligence, probabilistic model, RF suppression, verification, defense capability.
Abstract. The article presents the results of an analysis of regulatory documents governing the application of ultrasonic diagnostic methods for machines and mechanisms, as well as a comparison with current standards in the field of acoustic frequencies. This approach made it possible to identify differences between areas of application and to determine the possibilities of practical use of existing standards in the development of new diagnostic devices. Special attention is given to the review of ultrasonic frequency measuring instruments currently available on the market. It was found that most of these devices have limited functional capabilities and a high cost. For example, some instruments are capable of detecting ultrasonic signals and indicating the direction of their source; however, visualization is limited to displaying a localized point, which does not allow the recording of physical parameters of the signal or the dynamics of its propagation in the medium. These limitations significantly reduce the potential for comprehensive diagnostics. The main objective of the article is to establish requirements for the characteristics of a next-generation device for ultrasonic diagnostics of mechanisms, which should combine the functions of recording frequency parameters and spatial visualization of the ultrasonic field. It is shown that existing devices are focused only on detecting the signal source. The need for developing new-generation instruments is substantiated. Such instruments would enable the visualization of the ultrasonic field in space and ensure more comprehensive diagnostics of machines and mechanisms, including early detection of hidden defects and potential failures. For the aviation industry, meeting these requirements is of particular importance, as it is directly related to ensuring a high level of flight safety.
Keywords: ultrasonic diagnostics, nondestructive testing (NDT), aviation safety, MEMS microphones, technical diagnostics, coustic visualization, instrument requirements, mechanism reliability.