A deep learning‐based approach for pseudo‐satellite positioning

Autor: Shuang Li, Jiacheng Wang, Baoguo Yu, Hantong Xing, Shuang Wang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Communications, Vol 18, Iss 17, Pp 1140-1150 (2024)
Druh dokumentu: article
ISSN: 1751-8636
1751-8628
DOI: 10.1049/cmu2.12821
Popis: Abstract Traditional pseudo‐satellite‐based indoor positioning techniques are greatly affected by the presence of multipath effects, leading to a notable reduction in the positioning precision. In order to tackle this challenge, a pseudo‐satellite indoor positioning method based on deep learning is proposed. The method grids the localization region, thus transforming positioning from a regression problem to a classification problem in the gridded areas. 1D‐convolutional neural network is employed to extract the correlation between pseudo‐satellite data and the positioning of indoor areas. Data are collected and the method is validated in three types of areas of the experimental field, namely unobstructed area, semi‐unobstructed area and obstructed area. The experimental results demonstrate that the method exhibits superior positioning accuracy compared to traditional methods, enabling effective localization even in obstructed area.
Databáze: Directory of Open Access Journals
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