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

Sensors

Yong-Hwa Kim¹  Byeong-ho Lee²  Kyoung-Min Park²  Seong-Cheol Kim² 

[1] Department of Data Science, Korea National University of Transportation, Uiwang-si 16106, Korea;Department of Electrical and Computer Engineering, Institute of New Media & Communications, Seoul National University (SNU), Seoul 08826, Korea;

关键词: dual-band;    indoor localization;    range-based localization;    received signal strength;    trilateration;    Wi-Fi;   

DOI  :  10.3390/s21165583

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown