Radars are expected to become the main sensors in various civilian
applications, ranging from health-care monitoring to autonomous driving.
Their success is mainly due to the availability of both low cost
integrated devices, equipped with compact antenna arrays, and
computationally efficient signal processing techniques. An increasingly
important role in the field of radar signal processing is played by
machine learning and deep learning techniques. Their use has been first
taken into consideration in human gesture and motion recognition, and in
various healthcare applications. More recently, their exploitation in
object detection and localization has been also investigated. The
research work accomplished in these areas has raised various technical
problems that need to be carefully addressed before adopting the above
mentioned techniques in real world radar systems. In this manuscript, a
comprehensive overview of the machine learning and deep learning
techniques currently being considered for their use in radar systems is
provided. Moreover, some relevant open problems and current trends in
this research area are analysed. Finally, various numerical results,
based on both synthetically generated and experimental datasets, and
referring to two different applications are illustrated. These allow
readers to assess the efficacy of specific methods and to compare them
in terms of accuracy and computational effort.