Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. 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. 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 main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals.
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.
However, challenges remain with respect to both complexity and accuracy of such systems. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. The last few decades have witnessed a growing interest in location-based services. proposes a fingerprint-based single-site localization method for massive-MIMO OFDM systems. This paper provides a review of the history of localization, the main technological enablers of localization and assesses the future directions of localization methods. Tracking technologies are also considered key components for localization, as are the more recently integrated concepts of machine learning and artificial intelligence. These positioning technologies include the Global Positioning System (GPS), WiFi-based indoor localization, cell-phone based localization (including the fusion of GPS, cell-tower based localization and dead-reckoning), and inertial/dead-reckoning techniques. Accordingly, position location technologies have become key components of many worldwide industries. In fact, in many aspects of modern life, localization systems are deemed essential to day-to-day living and are contributing to our general well-being, the economy, and security. They have also facilitated worldwide services provided by companies such as Uber and Lyft.
For example, navigation systems are the enablers of road traffic prediction, assisted and autonomous driving, and several aspects of healthcare. Today, positioning technologies have deeply merged with daily life and enabled many novel sensors, systems and services. Prior to the 21st century, positioning technologies had limited applications including air traffic control, air and sea navigation, satellite communications and related military uses.