Radar Signal Processing: A Comprehensive Review

Abstract

Signal processing is at the heart of modern radar system functionality, enabling the accurate extraction of target information in environments filled with clutter and noise. This article presents a comprehensive review of the evolution and current state of radar signal processing techniques, ranging from fundamental principles to cutting-edge innovations. The discussion begins by dissecting the basic framework of data processing, including waveform design, pulse compression, and processing of the radar data cube. We evaluate classical detection algorithms such as Constant False Alarm Rate (CFAR) and super-resolution parameter estimation techniques. Furthermore, this article examines advancements in array processing, particularly in Multiple-Input Multiple-Output (MIMO) systems and Space-Time Adaptive Processing (STAP), which are crucial for mitigating complex interference. A key part of this review highlights the integration of Machine Learning and Deep Learning into cognitive radar schemes, as well as the emergence of Compressive Sensing technology for data efficiency. We also explore new paradigms such as radar-communication convergence (RadCom) within the 6G ecosystem and the potential of quantum radar. In conclusion, this article identifies open challenges, including interference management in automotive radar and the need for real-time computation for targets with low Radar Cross Section (RCS), to provide strategic guidance for future research.