5G Indoor Positioning Error Correction Based on 5G-PECNN

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 6; p. 1949
• Main Authors: Yang, Shan, Zhang, Qiyuan, Hu, Longxing, Ye, Haina, Wang, Xiaobo, Wang, Ti, Liu, Syuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19-03-2024; MDPI
• Subjects: 5G indoor positioning; Accuracy; Algorithms; Antennas; Arrays; Communication; Communications networks; Computer simulation; Datasets; Deep learning; Distance learning; Error correction & detection; indoor positioning error correction; Localization; Methods; Music; neural network; Neural networks; Principles; Receivers & amplifiers; Satellites; Technology application; indoor positioning error correction; neural network; 5G indoor positioning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24061949

With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.