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arXiv Machine Learning By Christopher J. Noroozi, Joseph L. Woodgate, Michael Mangan, Michael T. Smith

Ultra Low-Power, Lightweight, Probabilistic RSS-Based Path Reconstruction: A System for Landscape-Scale Bee Tracking

Read the original on arXiv Machine Learning →

The paper presents a new RSS‑based localization system that tracks ultra‑light, low‑power receivers across complex landscapes. By using a minimal number of RSS measurements from rotating high‑gain transmitters and probabilistic modeling, the system achieves about 15 m accuracy for 38 mg receivers over a 300 m range while consuming less than 180 µW. Increasing RSS measurements improves accuracy to roughly 10 m at under 600 µW, and the method is demonstrated on tracking Bombus terrestris nest return flights.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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