ArticleSensors (Basel, Switzerland)2019
An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Bayesian optimized indoor positioning algorithm based on dual clustering.Scientific reports · 2025Article
- Affect under need satisfaction and need thwarting: A new classification for the prediction of creative performance.Heliyon · 2024Article
- Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images.Micromachines · 2023Article
- Webcast marketing platform optimization via 6G R&D and the impact on brand content creation.PloS one · 2023Article
- Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM.PloS one · 2022Article
- Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building Indoor Optimization Design.Computational intelligence and neuroscience · 2022Article
- An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm.Sensors (Basel, Switzerland) · 2021Article
- Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments.Sensors (Basel, Switzerland) · 2021Article
- An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window.Sensors (Basel, Switzerland) · 2020Article
- An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals.Sensors (Basel, Switzerland) · 2020Article
- A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part I: Fundamentals and Enabling Technologies.IEEE access : practical innovations, open solutions · 2020Article
- An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.