Beyond GNSS: A Survey and Tutorial on Satellite-Based Radio Frequency (RF) Geolocation and Emitter Fingerprinting

The ubiquitous growth of connected devices in emerging Internet of Things (IoT) and 5G/6G networks has intensified the demand for secure, wide-area localization and identification. Although the Global Navigation Satellite System (GNSS) remains the dominant positioning solution, its susceptibility to jamming, spoofing, and signal blockage calls for resilient alternatives and supplements. Satellite-based Radio Frequency (RF) geolocation could provide a global coverage by exploiting uplink communication emissions, while RF fingerprinting enables device authentication through unique physical-layer features of the transmitter. This paper presents a unified tutorial and survey of satellite-based RF geolocation, fingerprinting, and classification, addressing both questions where an emitter is and who it is. Classical measurement-based methods, including Received Signal Strength (RSS), Time Difference of Arrival (TDoA), Doppler signatures, and Angle of Arrival (AoA) are reviewed alongside extrinsic and intrinsic fingerprinting strategies. We further examine classification frameworks leveraging Machine Learning (ML) and Deep Learning (DL) to identify and cluster emitters under diverse channel conditions. Applications in spectrum monitoring, interference mitigation, and large-scale IoT security are discussed, with emphasis on GNSS-denied and adversarial environments. This work integrates spectrum sensing, geolocalization, and fingerprinting within a unified framework and introduces a geolocalization open-source MATLAB simulation that enables reproducible benchmarking and community-driven extension.