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MAPLE-RF: Efficient Probabilistic RF Source Localization in Partially Explored Environments

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Localizing a radio-frequency (RF) transmitter from received signals often requires a model of the environment to predict how obstacles block and reflect the signal. In many robotic applications, however, only a partial map is available, particularly when a robot localizes the source while exploring with simultaneous localization and mapping (SLAM). We study single-snapshot transmitter localization on such partially explored maps and compare two approaches that output a posterior over transmitter locations. The first extends a digital-twin method, which ray-traces every candidate location, to p

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