ArticleSensors (Basel, Switzerland)2021
An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2025Review
- Bayesian optimized indoor positioning algorithm based on dual clustering.Scientific reports · 2025Article
- A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System.Sensors (Basel, Switzerland) · 2023Review
- Analysis and Accuracy Improvement of UWB-TDoA-Based Indoor Positioning System.Sensors (Basel, Switzerland) · 2022Article
- Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
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Abstract
Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.
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