Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning

• Published in: Scientific reports Vol. 14; no. 1; pp. 13121 - 12
• Main Authors: Guo, Hua, Yin, Haozhou, Song, Shanshan, Zhu, Xiuwei, Ren, Daokuan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/705/117; Accuracy; Algorithms; Clustering; Environmental factors; Humanities and Social Sciences; Localization; multidisciplinary; Optimization algorithms; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63358-4

Due to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the influence of these environmental factors and improve the accuracy of indoor wireless positioning, this paper proposes a density clustering with noise combined with particle swarm optimization (DCNPSO) to improve UWB positioning. Which exploits the advantages of the density-based spatial clustering algorithm with noise (DBSCAN) and particle swarm optimization (PSO) algorithm. The experimental results show that the DCNPSO algorithm achieves 45.25% and 36.14% higher average positioning accuracy than the DBSCAN and PSO algorithms, respectively. The positioning error of this algorithm remains stable within 3 cm in static positioning and can achieve high accuracy in NLOS environments.