Comprehensive Survey on Radar Systems and Its Target Classification Techniques

Abstract

This paper surveys target classification techniques in radar systems, focusing on the transformative role of artificial intelligence in enhancing detection and classification capabilities. It reviews the evolution of radar architectures, emphasizing their design, functionality, and key parameters that drive performance. The study spans a range of approaches, from traditional machine learning to advanced deep learning methods, including CNNs, RNNs, self-attention mechanisms, vision transformers, and 2D-SPS. These innovations enable breakthroughs in micromotion detection, background noise reduction, and prediction accuracy. By highlighting applications across various industries, this work provides valuable insights to researchers and engineers, paving the way for advancements in radar technology driven by robust hardware and sophisticated algorithms.