Güç-Zaman Görüntülerini Kullanan Derin Evrişimsel Sinir Ağı Tabanlı Anten Tarama Tipi Sınıflandırma

In Electronic Warfare (EW) systems, one of the most critical tasks is the accurate identification of radars in the environment. During the identification process, various uncertainties may arise. Antenna Scan Type (AST) serves as a significant parameter for resolving these ambiguities. In this study, a method based on Deep Convolutional Neural Networks (DCNN) with input as power-time images is proposed for the AST classification problem. Unlike previous studies in the literature, this method can operate independently of the number of pulses. The classification process was conducted using a DCNN-based model, and the results were shared with the readers Upon examination of the test results, it is observed that the proposed method yields successful outcomes for the AST classification problem.