A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
Daoud Burghal, Ashwin T. Ravi, Varun Rao, Abdullah A. Alghafis,, Andreas F. Molisch

TL;DR
This paper provides a comprehensive survey of machine learning-based localization techniques using RF signals, covering system architectures, features, ML methods, datasets, and the interaction with wireless physics, summarizing insights from nearly 400 papers.
Contribution
It offers an extensive overview of ML-based localization solutions, highlighting the interplay between domain knowledge and ML approaches, and discusses various features, methods, and datasets in detail.
Findings
Categorizes and summarizes insights from nearly 400 papers.
Discusses the impact of features and preprocessing on localization accuracy.
Provides a self-contained review of ML and wireless propagation concepts.
Abstract
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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Underwater Vehicles and Communication Systems · Speech and Audio Processing
