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Fingerprint Database Reconstruction Based on Robust PCA for Indoor Localization.

Lingwen Zhang1, Teng Tan2, Yafan Gong3

  • 1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. zhanglw@bjtu.edu.cn.

Sensors (Basel, Switzerland)
|June 6, 2019
PubMed
Summary
This summary is machine-generated.

This study reconstructs indoor Received Signal Strength (RSS) fingerprint databases by using Robust Principal Component Analysis (RPCA) to remove noise and outliers. This improves the accuracy of indoor localization systems.

Keywords:
RSSRobust PCAindoor localizationlow rankoutlier

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Indoor localization systems often use Received Signal Strength (RSS) fingerprinting for accuracy and cost-effectiveness.
  • Radio signal propagation in indoor environments is complex, introducing outliers and noise into RSS fingerprint databases.
  • Degraded fingerprint databases significantly impair indoor localization system performance.

Purpose of the Study:

  • To propose a method for reconstructing RSS fingerprint databases to mitigate the impact of outliers and noise.
  • To enhance the performance and reliability of indoor localization systems.

Main Methods:

  • Exploiting spatial and temporal correlations in RSS data to transform the database into a low-rank matrix.
  • Applying Robust Principal Component Analysis (RPCA) to recover the low-rank matrix from noisy data.
  • Developing an improved RPCA model incorporating singular value prior knowledge for simultaneous outlier and structured noise removal.

Main Results:

  • The proposed method effectively reconstructs the fingerprint database by removing outliers and structured noise.
  • Experimental results demonstrate the efficiency of the improved RPCA model in cleaning RSS data.
  • The reconstructed database leads to improved indoor localization accuracy.

Conclusions:

  • The developed RPCA-based approach offers an efficient solution for cleaning RSS fingerprint databases.
  • Mitigating outliers and structured noise is crucial for maintaining high performance in indoor localization.
  • The proposed method enhances the robustness and accuracy of RSS fingerprinting techniques.