Advances in Radar Signal Processing: Integrating Deep Learning Approaches

Abstract

In the military, in daily life, and in scientific research, radar technology is widely used. Radar signal processing has long been an essential component in target detection and imaging. The development of deep learning technology in recent years has given radar signal processing new approaches and resources. With its exceptional feature extraction and pattern recognition skills, deep learning has made amazing strides and has been applied to radar signal processing to enhance tasks like target detection, tracking, and recognition. Traditional radar signal processing is based on models. It mainly uses the prior information of the model and related signal processing criteria to design signal processing methods. It uses Gaussian, linear, and stationary assumptions. The deep learning method is a data-based method that does not require prior knowledge of the model and can spontaneously find the relationship between the input of the algorithm and the expected output. This article will review traditional methods and deep learning methods in radar signal processing, focusing on the application and future development direction of deep learning methods in radar signal processing, briefly sorting out the research progress in recent years, and analyze some existing problems or shortcomings of existing methods.