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Related Experiment Video

Updated: May 23, 2025

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

A device free high-precision indoor positioning and tracking method based on GMM-WKNN algorithm.

Mumen Jiang¹, Jiangnan Yuan²

¹School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361000, China.

Scientific Reports

|May 21, 2025

Summary

This study introduces a novel Wi-Fi Channel State Information (CSI) indoor localization and tracking algorithm. The method enhances accuracy by integrating Gaussian Mixture Models (GMM), Weighted K-Nearest Neighbors (WKNN), and Kalman filtering for precise passive tracking.

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Area of Science:

Wireless communication
Signal processing
Indoor positioning

Background:

Current indoor localization and tracking methods suffer from high deployment complexity and algorithmic intricacies.
There is a need for accurate and efficient passive tracking solutions in indoor environments.

Purpose of the Study:

To develop a Wi-Fi CSI-based indoor localization and tracking algorithm that overcomes the limitations of existing methods.
To enhance tracking accuracy and reduce complexity through an integrated GMM, WKNN, and Kalman filtering approach.

Main Methods:

Utilized Gaussian Mixture Model (GMM) for offline fingerprint database creation and online Channel State Information (CSI) data clustering.
Employed Weighted K-Nearest Neighbors (WKNN) for initial trajectory estimation based on categorized CSI data.
Applied Kalman filtering for refining trajectory estimation and achieving passive tracking.

Main Results:

The GMM effectively captures complex indoor channel characteristics, offering significant filtering and noise reduction.
The proposed algorithm demonstrated improved tracking accuracy compared to traditional methods and CNN-based approaches.
Successful passive tracking of individuals indoors was achieved.

Conclusions:

The integrated GMM-WKNN-Kalman filter approach provides an effective solution for Wi-Fi CSI-based indoor localization and tracking.
This method offers enhanced accuracy and reduced complexity for passive indoor tracking applications.
The algorithm's ability to handle complex channel characteristics makes it suitable for diverse indoor environments.