MAGNETAR: Multipath-Guided Spatial Posteriors for Transmitter Pose Inference in the Upper Mid-Band

2026-09-17

AI Summary

MAGNETAR通过单次RF多路径快照和房间布局，利用2D U-Net神经网络推断发射器位置与方向的联合后验分布，解决机器人在复杂环境中定位无线电发射器的问题。

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.

Problem

• Transmitter Pose Inference
• Multipath
• Upper Mid-Band

Innovation

• Multipath-Guided
• Spatial Posteriors
• Transmitter Pose Inference
• U-Net
• Joint Normalization