Gelis tirilmis Transformer Sinir Aglari ile Radar Hedef Tespiti

In this study, a method based on the improved Vision Transformer (ViT) architecture is proposed for radar target detection instead of traditional signal processing techniques. Instead of the conventional Multi-Layer Perceptron (MLP) structure, an advanced network architecture has been employed to enhance target detection performance in cluttered environments. The study utilizes both synthetic and real data. The proposed method has been compared with SO-CA CFAR, GO-CA CFAR, CA-CFAR, OS-CFAR, and CNN in terms of target detection accuracy. The results indicate that the proposed approach outperforms traditional CFAR methods and the deep learning-based CNN method, particularly in the presence of clutter.