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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Indoor Localization Algorithm Based on a High-Order Graph Neural Network.

Xiaofei Kang¹, Xian Liang¹, Qiyue Liang¹

¹College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

A new high-order graph neural network (HoGNNLoc) improves indoor localization accuracy and stability. This machine learning approach effectively handles complex radio signal data, outperforming existing methods for reliable positioning.

Keywords:

Wi-Fi fingerprint adjacency matrix graph neural network indoor localization

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Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

Machine learning fingerprinting is common for indoor localization.
Existing models struggle with unstructured fingerprint data from diverse environments.
Need for enhanced accuracy and stability in dynamic indoor settings.

Purpose of the Study:

Propose a novel indoor localization algorithm, HoGNNLoc.
Enhance accuracy and stability of indoor localization.
Address limitations of current machine learning models for fingerprint data.

Main Methods:

Developed a high-order graph neural network (HoGNNLoc) for indoor localization.
Constructed a graph structure using access point spatial locations.
Employed a fully connected network for location regression prediction.

Main Results:

Achieved localization accuracy within 1.29m at 80% CDF on a self-built dataset.
Demonstrated significant accuracy improvements over KNN, DNN, SGC, and GAT (up to 59.2%).
Maintained stable performance even with a 30% reduction in fingerprint data.

Conclusions:

HoGNNLoc offers superior indoor localization performance.
The algorithm effectively extracts features from complex fingerprint data.
HoGNNLoc shows promise for robust and accurate indoor positioning systems.