Enhancing ATC Radar System Reliability: Strategies and Modern Solutions

This paper explores advanced strategies for improving ATC radar system reliability by addressing interference challenges from airborne systems such as ACAS, DME, and ADS-B, as well as environmental influences. Proposed solutions include the integration of autonomous receivers, hybrid radar architectures, and machine learning models for enhanced signal processing. Additionally, the paper examines innovative algorithms for real-time compensation of ionospheric distortions and atmospheric influences, ensuring precise long-range detection. The study demonstrates how modern techniques improve radar performance, reduce false alarms, and enhance detection accuracy. Future research should focus on integrating ADS-B and multi-positioning systems into ATC structures while optimizing compensation algorithms to ensure operational efficiency. 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.