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

2026-09-17

AI Summary

研究在部分探索环境中使用MAPLE-RF和改进的数字孪生方法解决RF源定位问题，通过估计信号路径角度和信噪比来提高效率与准确性。

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.

Problem

• RF Source Localization
• Partially Explored Environments
• Simultaneous Localization and Mapping (SLAM)
• Digital-Twin Method
• MAPLE-RF

Innovation

• MAPLE-RF
• Partial Maps
• U-Net
• Efficient Localization
• Probabilistic