ArticleSensors (Basel, Switzerland)2016
Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Survey of Recent Indoor Localization Scenarios and Methodologies.Sensors (Basel, Switzerland) · 2021Review
- Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services.Sensors (Basel, Switzerland) · 2020Article
- Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.Sensors (Basel, Switzerland) · 2018Article
- Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples.Sensors (Basel, Switzerland) · 2018Article
- Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing.Sensors (Basel, Switzerland) · 2018Article
- A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm.Sensors (Basel, Switzerland) · 2018Article
- EKF-GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity.Sensors (Basel, Switzerland) · 2018Article
- LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning.Sensors (Basel, Switzerland) · 2017Article
- Gaussian Process Regression Plus Method for Localization Reliability Improvement.Sensors (Basel, Switzerland) · 2016Article
- Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances.Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs' RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user's location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.
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