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Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm.

Fang Cheng1, Guofeng Niu1, Zhizhong Zhang1

  • 1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

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Summary
This summary is machine-generated.

This study enhances Wi-Fi fingerprinting for indoor positioning. By fusing RSS and angle of arrival data and using DBSCAN with CNNs, it improves accuracy and stability in locating users.

Keywords:
DBSCANWi-Fi fingerprintsconvolution neural network (CNN)indoor location

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Area of Science:

  • * Wireless communication and signal processing.
  • * Artificial intelligence and machine learning for localization.

Background:

  • * Increasing demand for indoor positioning services driven by wireless system deployment and intelligent devices.
  • * Wi-Fi fingerprinting is common but suffers from unstable performance and noise sensitivity.
  • * Convolutional Neural Networks (CNNs) improve precision but are sensitive to the number of reference points used.

Purpose of the Study:

  • * To develop a more stable and accurate indoor positioning method using Wi-Fi fingerprinting.
  • * To address the limitations of traditional methods regarding reference point selection.
  • * To enhance positioning precision by fusing signal strength and angle of arrival data.

Main Methods:

  • * Fusing grayscale images of Received Signal Strength (RSS) and Angle of Arrival (AoA) into RGB images for improved stability.
  • * Employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to analyze CNN output and select optimal reference points.
  • * Approximating final position using weighted k-nearest neighbors.

Main Results:

  • * The proposed method demonstrates improved stability and accuracy compared to traditional techniques.
  • * DBSCAN effectively selects appropriate reference points, overcoming limitations of fixed selection methods.
  • * Achieved calculation errors at least 0.1-0.3 m less than traditional methods.

Conclusions:

  • * The fusion of RSS and AoA data, combined with DBSCAN and CNNs, significantly enhances indoor positioning accuracy.
  • * The adaptive reference point selection mechanism improves the robustness of Wi-Fi fingerprinting.
  • * This approach offers a more reliable solution for precise indoor localization.