Abstract <p>Target identification is one of the fundamental challenges in radar systems, involving the classification of detected objects based on their type (e.g., human, vehicle, or other living beings) and motion state (stationary or moving). Traditional approaches rely on analyzing variations in the target’s motion vector parameters. However, advancements in computational power and parallel processing algorithms have enabled the integration of artificial intelligence methods to enhance identification accuracy and efficiency. While artificial intelligence-based approaches require extensive and computationally intensive training phases, their inference process is typically fast and scalable. In this work, we propose a machine learning-based target identification framework that leverages convolutional neural networks. Specifically, we investigate the application of classical convolutional layers to process radio-frequency images. An analysis of this architecture is performed to evaluate its effectiveness in radar-based classification tasks.</p>