ArticleSensors (Basel, Switzerland)2018
Indoor Positioning Algorithm Based on the Improved RSSI Distance Model.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings.PLOS digital health · 2025Article
- Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.Sensors (Basel, Switzerland) · 2024Article
- An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes.Infectious Disease Modelling · 2024Article
- BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement.Sensors (Basel, Switzerland) · 2024Article
- Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization.Sensors (Basel, Switzerland) · 2023Article
- Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for Enhancement of Location Recognition.Sensors (Basel, Switzerland) · 2023Article
- Smart Gateway for Healthcare Networks Based on Beam Steering Technology.Sensors (Basel, Switzerland) · 2023Article
- A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization.Sensors (Basel, Switzerland) · 2023Article
- Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.Neural computing & applications · 2023Article
- Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2022Article
- Review of Navigation Assistive Tools and Technologies for the Visually Impaired.Sensors (Basel, Switzerland) · 2022Review
- Precision Enhancement of Wireless Localization System Using Passive DOA Multiple Sensor Network for Moving Target.Sensors (Basel, Switzerland) · 2022Article
- An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning.Sensors (Basel, Switzerland) · 2022Article
- Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments.Scientific data · 2022Article
- Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN.Sensors (Basel, Switzerland) · 2022Article
- Public Policies in the informational era: a compared perspective in the context of pandemic in two Lusophone countries.Einstein (Sao Paulo, Brazil) · 2022Article
- Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections.Sensors (Basel, Switzerland) · 2022Article
- Combining Indoor Positioning Using Wi-Fi Round Trip Time with Dust Measurement in the Field of Occupational Health.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.
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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.