Analysis of Negative Impacts On Air Traffic Control Radar Systems and Modern Methods For Their Elimination

This paper explores comprehensive strategies for improving radar system reliability in ATC environments. It examines state-of-the-art methodologies to address interference challenges posed by onboard systems like the ACAS, DME and ADS-B, as well as atmospheric influences on ATC radars signal propagation. The proposed solutions include the integration of standalone autonomous receivers, hybrid radar architectures, and machine-learning models for adaptive signal processing. Additionally, the paper examines innovative algorithms for real-time compensation of ionospheric distortions and atmospheric influences, ensuring precise long-range detection. Modeling and data analysis highlight the impact of interference on radar systems, leading to false alarms, signal degradation, and measurement errors. The structural models considered in the work show that autonomous receivers are capable of detecting false alarms and thereby increasing the reliability of radar information, and hybrid radar systems effectively suppress interference and improve target tracking. Implementations of atmospheric compensation algorithms show promising results in minimizing errors caused by these factors. Additionally, machine learning applications have been shown to improve signal classification accuracy and adaptability in dynamic environments. The results obtained highlight the need to modernize ATC radar systems to address growing air traffic density and the growing prevalence of airborne interference sources. It is shown that future directions require studying the integration of new technologies such as ADS-B and multilateration into the АТС structure, optimizing ionospheric and atmospheric compensation algorithms, and conducting tests to validate these solutions. By addressing these challenges, the proposed methodologies ensure enhanced safety margins and operational efficiency for the aviation sector.