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Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process
Qiang Chang1, Qun Li2, Zesen Shi3
1College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. changqiang@nudt.edu.cn.
Sensors (Basel, Switzerland)
|March 22, 2016
Summary
This study introduces a new indoor positioning algorithm that creates a virtual signal database, improving accuracy by 25.5% and enabling localization in unsurveyed areas. The method enhances Received Signal Strength Indication (RSSI) fingerprinting efficiency.
Area of Science:
- * Computer Science
- * Electrical Engineering
- * Geomatics Engineering
Background:
- * Indoor localization relies heavily on Received Signal Strength Indication (RSSI) fingerprinting, which requires dense signal databases for accuracy.
- * Building and maintaining these databases is resource-intensive and impractical for many areas, limiting current algorithms.
- * Existing methods struggle in areas lacking calibration data, necessitating more efficient database creation and update strategies.
Purpose of the Study:
- * To develop a scalable indoor positioning algorithm effective in both surveyed and unsurveyed regions.
- * To address the limitations of traditional RSSI fingerprinting by reducing reliance on dense, manually created signal databases.
- * To enhance indoor localization accuracy and coverage through novel virtual database construction and estimation techniques.
Main Methods:
- * Proposed the Minimum Inverse Distance (MID) algorithm to construct a virtual database with uniformly distributed virtual Reference Points (RPs).
- * Employed a Local Gaussian Process (LGP) to estimate RSSI values for virtual RPs using crowdsourced training data.
- * Enhanced a Bayesian algorithm for user location estimation utilizing the generated virtual database.
Main Results:
- * Achieved a 25.5% improvement in positioning accuracy within surveyed areas compared to existing methods.
- * Demonstrated an average positioning error below 2.2 meters for 80% of test cases.
- * Successfully enabled indoor localization in neighboring unsurveyed areas, expanding the applicability of the algorithm.
Conclusions:
- * The proposed scalable algorithm significantly enhances indoor positioning accuracy and efficiency.
- * It overcomes the limitations of traditional fingerprinting by creating virtual databases, reducing manual effort.
- * The method offers practical solutions for indoor localization in diverse environments, including those without prior calibration data.
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