  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.
