ArticleSensors (Basel, Switzerland)2020
An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window.
Article in Sensors (Basel, Switzerland), 2020. 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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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.
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Who cites it
3 citing papers in PubMed, 21 citations in OpenAlex.
- UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.Sensors (Basel, Switzerland) · 2024Article
- Bluetooth-Based Healthcare Information and Medical Resource Management System.Sensors (Basel, Switzerland) · 2023Article
- A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.
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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.