外观
论文arXiv 2609.210261 个月前
MAPLE-RF：部分探索环境中的高效概率式RF源定位
原文链接：MAPLE-RF: Efficient Probabilistic RF Source Localization in Partially Explored Environments（arXiv 2609.21026 · 发布日期：2026-09-17）
MAPLE-RF: Efficient Probabilistic RF Source Localization in Partially Explored Environments
研究部分地图上的单快照RF发射源定位，提出MAPLE-RF以U-Net一次性评分，并在室内射线追踪仿真中验证。
核心要点
- RF发射源定位常需环境模型；机器人SLAM探索时常只有部分地图可用。
- 比较两种输出位置后验的方法：部分地图数字孪生扩展与MAPLE-RF。
- MAPLE-RF把到达角、信噪比及地图相关通道对齐，用U-Net一次评分全候选。
- 室内仿真显示混合覆盖训练必要；MAPLE-RF精度接近但查询成本低得多，代码数据将发布。
验证范围与局限：验证限于室内环境的射线追踪仿真、单快照定位和由估计引导的探索路线。
英文摘要 (Abstract)
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 partial maps by treating unexplored space as free and training on mixed map coverage. The second, MAPLE-RF, encodes estimated path angles of arrival and signal-to-noise ratios as grid channels aligned with map knownness, occupancy, and line-of-sight visibility, and a U-Net scores all candidate positions in one pass without simulating propagation at inference. Ray-tracing simulations of indoor environments indicate that training on mixed map coverage is essential for both approaches. The digital-twin approach is more accurate on most single-snapshot metrics, while MAPLE-RF comes close at a query cost that does not depend on the propagation model and is more than two orders of magnitude below a fresh full-grid query with general-purpose ray tracing. Both outperform Gaussian and Gaussian-mixture baselines, and on exploration routes guided by its own estimates, fused MAPLE-RF posteriors place more probability near the source than the compared methods. Code and data will be released.
作者：Haozhe Lei, Sundeep Rangan
来源
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