{"ID":23507392,"CreatedAt":"2026-09-18T02:21:44.056544415Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20670","arxiv_id":"2609.20670","title":"MAGNETAR: Multipath-Guided Spatial Posteriors for Transmitter Pose Inference in the Upper Mid-Band","abstract":"Robots that localize a radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitter locations and, because upper-mid-band antennas are directional, several headings. We present MAGNETAR, which infers a joint posterior over planar transmitter position and heading from a single asynchronous radio-frequency (RF) multipath snapshot, represented by angle-of-arrival and signal-to-noise-ratio estimates, given the room layout and receiver pose. Among our five neural scorers, MAGNETAR adopts a shared 2D U-Net conditioned on each candidate heading, jointly normalizing scores over a discretized position-heading grid. Training uses real-to-sim-calibrated 10 GHz simulations and a small measured subset. Grid-based joint posteriors outperform parametric ones on held-out simulations, the heading-conditioned scorer transfers best to robotic measurements, and fusing joint posteriors improves on fusing position-only marginals.","short_abstract":"Robots that localize a radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitter locations and, because upper-mid-band antennas are directional, several headings. We present MAGNETAR, which infers a joint posterior over planar transmitter positio...","url_abs":"https://arxiv.org/abs/2609.20670","url_pdf":"https://arxiv.org/pdf/2609.20670v1","authors":"[\"Haozhe Lei\",\"Ruibin Chen\",\"Yuhan Jiang\",\"Ali Rasteh\",\"Aditya Dhananjay\",\"Sundeep Rangan\"]","published":"2026-09-17T16:44:59Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SP\"]","methods":"[]","has_code":false}