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UWB tag NLOS detection methods compared for on-device accuracy and efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated methods for detecting wearer-induced non-line-of-sight (NLOS) conditions for ultra-wideband (UWB) tags worn on the chest. The study compares on-device machine learning (ML) classifiers with UWB radio link indicators (LI), exploring hybrid approaches that combine their strengths. Analysis of 1.5 million samples across various configurations revealed that while most classifiers achieve good accuracy, their performance and energy efficiency vary significantly, with some consuming up to 39.6 times more energy for marginal gains. A recommender tool has been developed to help users select optimal classifiers based on their specific constraints. AI

IMPACT Provides insights into optimizing on-device ML for resource-constrained applications like wearable sensors.

RANK_REASON Academic paper detailing a novel methodology and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

UWB tag NLOS detection methods compared for on-device accuracy and efficiency

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Academic paper detailing a novel methodology and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Markus Gallacher, Vu Anh Minh Le, Matteo Trobinger, Davide Vecchia, Carlo Alberto Boano, Gian Pietro Picco ·

    On-device Detection of Wearer-induced NLOS on Chest-worn UWB Tags: Machine Learning, Link Indicators, or Both?

    arXiv:2610.41799v1 Announce Type: new Abstract: Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions. When UWB tags are worn, the human body inevitably causes a wearer-induced NLOS (WNLOS), yield…

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