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.