A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

TL;DR

This survey reviews how machine learning, especially deep learning, is increasingly used to improve radar signal processing, addressing traditional limitations in target classification and real-time performance.

Contribution

It provides a comprehensive overview of ML-based radar signal processing techniques, structured by application field, highlighting current gaps and future research directions.

Findings

ML enhances radar target classification accuracy

Deep learning models improve real-time processing capabilities

Identifies open challenges and future research directions in ML-RSP

Abstract

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.