ArticleScientific reports2024
Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision.Sensors (Basel, Switzerland) · 2025Article
- Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust.Sensors (Basel, Switzerland) · 2024Article
- Soft sensing modeling of penicillin fermentation process based on local selection ensemble learning.Scientific reports · 2024Article
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5 authors.
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Abstract
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.
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