Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses da...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (16), p.5583
Main Authors: Lee, Byeong-ho, Park, Kyoung-Min, Kim, Yong-Hwa, Kim, Seong-Cheol
Format: Article
Language:English
Subjects:
Accuracy
Communication
Deep learning
dual-band
Error reduction
indoor localization
Infrastructure
Localization
Localization method
Methods
Neural networks
Propagation
range-based localization
Ray tracing
received signal strength
Satellites
Signal strength
Smartphones
trilateration
Wi-Fi
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Summary:In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21165583