MAPLE-RF: 部分的に探索された環境における効率的な確率的RF源位置推定
MAPLE-RF: Efficient Probabilistic RF Source Localization in Partially Explored Environments
部分的な地図上で電波源の位置を推定するため、到来角とSNRを地図情報と組み合わせてU-Netで一度に全候補を評価する手法を提案し、レイトレーシングより2桁以上高速で高精度な推定を実現した。
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著者: Haozhe Lei, Sundeep Rangan
分類: cs.RO, eess.SP
原文アブストラクト
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.
