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Bayesian optimized indoor positioning algorithm based on dual clustering.

Min Chen^1,2, Qiaolin Pu³

¹School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.

Scientific Reports

|March 19, 2025

Summary

This study enhances Wi-Fi indoor positioning by using clustering algorithms to create sub-databases, improving efficiency and accuracy. The optimized Bayesian probabilistic algorithm significantly reduces runtime and boosts system stability.

Keywords:

Bayesian probabilistic algorithm Dual clustering Fingerprint database Fingerprint positioning

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Published on: February 15, 2017

Area of Science:

Computer Science
Electrical Engineering
Geomatics Engineering

Background:

Wi-Fi Received Signal Strength Indication (RSSI) fingerprinting offers a cost-effective indoor positioning solution.
Accuracy limitations (meter scale) and database inefficiency hinder current Wi-Fi positioning systems.
Complex indoor environments significantly impact signal propagation and positioning accuracy.

Purpose of the Study:

To improve the accuracy, efficiency, and real-time performance of Wi-Fi indoor positioning systems.
To address the challenges of large fingerprint databases and environmental signal interference.
To develop an optimized Bayesian probabilistic algorithm for enhanced indoor localization.

Main Methods:

Implemented Canopy and K-means clustering algorithms to create efficient sub-databases, reducing positioning time.
Utilized correlation coefficient method for selecting similar sub-databases during real-time matching.
Integrated Weighted K-Nearest Neighbors (WKNN) with an improved Bayesian probabilistic optimization algorithm.

Main Results:

Positioning efficiency improved by approximately 95.05% through clustering.
Average positioning accuracy increased by about 38.64%.
Average runtime reduced by approximately 93.51%, with slight improvements in system stability.

Conclusions:

The proposed clustering and optimized Bayesian probabilistic algorithm effectively enhances Wi-Fi indoor positioning accuracy and real-time performance.
The method significantly reduces computational load and improves system stability.
This approach offers a more robust and efficient solution for indoor localization challenges.