A Deep Learning Approach for Generating Soft Range Information from RF Data
| Autor: | Yuxiao Li, Santiago Mazuelas, Yuan Shen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Indoor localization soft range information deep learning ranging error mitigation NLOS detection Machine Learning (cs.LG) |
| DOI: | 10.1109/gcwkshps52748.2021.9681832 |
| Popis: | Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning approach to generate accurate SRI from RF measurements. In particular, the proposed approach is implemented by a network with two neural modules and conducts the generation directly from raw data. Extensive experiments on a case study with two public datasets are conducted to quantify the efficiency in different indoor localization tasks. The results show that the proposed approach can generate highly accurate SRI, and significantly outperforms conventional techniques in both non-line-of-sight (NLOS) detection and ranging error mitigation. Comment: Published in: 2021 IEEE Globecom Workshops (GC Wkshps) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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