A Survey of Machine Learning Techniques for Signal Source Localization and Emitter Tracking

Signal source localization and emitter tracking constitute fundamental functions in communication systems, autonomous navigation, surveillance, and electronic warfare. Conventional approaches based on geometric modeling, including time difference of arrival (TDoA), angle of arrival (AoA), and received signal strength (RSS), often degrade in complex environments characterized by multipath propagation, non-line of sight (NLoS) conditions, and dynamic interference. Recent advances in machine learning (ML) have motivated the development of data-driven frameworks capable of learning nonlinear mappings directly from signal measurements, thereby enhancing adaptability, robustness, and real-time applicability. This review provides a structured survey of ML techniques for source localization and tracking in both radio frequency (RF) and acoustic domains. Structured lists and comparative analyses of simulators, methods, surveys, and datasets for acoustic and RF localization, are presented, alongside a unifying taxonomy. Feature engineering methods, ranging from time-frequency transforms to spatio-temporal encoding and multimodal fusion, are examined in terms of their role in generalization and efficiency. Comparative analyses highlight the relative strengths of shallow models, probabilistic frameworks, and deep neural architectures, as well as the integration of synthetic data and transfer learning. The review concludes with perspectives on next-generation localization systems.