  The last few decades have witnessed a growing interest in location-based
services. Using localization systems based on Radio Frequency (RF) signals has
proven its efficacy for both indoor and outdoor applications. However,
challenges remain with respect to both complexity and accuracy of such systems.
Machine Learning (ML) is one of the most promising methods for mitigating these
problems, as ML (especially deep learning) offers powerful practical
data-driven tools that can be integrated into localization systems. In this
paper, we provide a comprehensive survey of ML-based localization solutions
that use RF signals. The survey spans different aspects, ranging from the
system architectures, to the input features, the ML methods, and the datasets.
  A main point of the paper is the interaction between the domain knowledge
arising from the physics of localization systems, and the various ML
approaches. Besides the ML methods, the utilized input features play a major
role in shaping the localization solution; we present a detailed discussion of
the different features and what could influence them, be it the underlying
wireless technology or standards or the preprocessing techniques. A detailed
discussion is dedicated to the different ML methods that have been applied to
localization problems, discussing the underlying problem and the solution
structure. Furthermore, we summarize the different ways the datasets were
acquired, and then list the publicly available ones. Overall, the survey
categorizes and partly summarizes insights from almost 400 papers in this
field.
  This survey is self-contained, as we provide a concise review of the main ML
and wireless propagation concepts, which shall help the researchers in either
field navigate through the surveyed solutions, and suggested open problems.
