发表机构
New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MAGNETAR利用多路径快照和U-Net评分器，推断上中频段发射机位置与航向的联合后验，在仿真和实测中优于参数化方法。
AI 中文摘要
对无线电发射机进行定位的机器人需要的不仅仅是点估计：在杂乱房间中，一次测量通常与多个发射机位置一致，并且由于上中频段天线具有方向性，还与多个航向一致。我们提出MAGNETAR，它根据房间布局和接收机姿态，从单个异步射频（RF）多路径快照（由到达角和信噪比估计表示）推断平面发射机位置和航向的联合后验。在我们的五个神经评分器中，MAGNETAR采用共享的2D U-Net，以每个候选航向为条件，在离散的位置-航向网格上联合归一化分数。训练使用真实到仿真校准的10 GHz仿真数据和少量实测子集。基于网格的联合后验在保留仿真上优于参数化后验，航向条件评分器对机器人实测数据迁移最佳，融合联合后验优于仅融合位置边缘分布。
英文摘要
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.
