An Improved Method to Detect and Locate Airborne Radar Targets Using Space Time Adaptive Processing

Accurate detection of airborne radar targets is a challenging due to the presence of clutter, noise, and interference. This paper presents an improved method based on Space-Time Adaptive Processing (STAP) to enhance target detection and localization performance. The proposed approach utilizes adaptive filtering and covariance matrix estimation to suppress interference while improving the signal-to-interference-plus-noise ratio (SINR). The airborne Radar system model is implemented using MATLAB simulation, where realistic radar conditions are modelled. Performance evaluation demonstrates improved detection accuracy, reduced false alarms, and better target localization compared to conventional methods. The results highlight the effectiveness of the proposed approach for advanced radar applications in surveillance and defense systems.