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UHF RFID tag localization using pattern reconfigurable reader antenna.

Md Shakir Hossain1, Md Abu Saleh Tajin1, Kapil R Dandekar1

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PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for indoor localization using ultra high frequency (UHF) radio frequency identification (RFID) systems. The random forest regressor model achieved the lowest localization error, accurately pinpointing tag positions.

Keywords:
Indoor localizationInternet of Things (IoT)RFID reader antennamachine learningradio frequency identification (RFID)reconfigurable RFID reader

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Robotics

Background:

  • Passive ultra high frequency (UHF) radio frequency identification (RFID) tags offer potential for widespread indoor object tracking and localization.
  • Accurate indoor positioning is crucial for applications like logistics, asset management, and proximity-based services.

Purpose of the Study:

  • To develop and evaluate a machine learning-based method for precise indoor localization using UHF RFID technology.
  • To compare the performance of five different machine learning regression models for RFID localization accuracy.

Main Methods:

  • A pattern reconfigurable UHF RFID reader antenna array was employed.
  • Received Signal Strength Indicator (RSSI) values from 10,000 RFID tags were used as input features.
  • A wireless ray tracing simulator generated training (75%) and testing (25%) data.
  • Five regression models were evaluated: Random Forest, Decision Tree, Nu-Support Vector Regressor, K-Nearest Neighbors, and Kernel Ridge Regressor.

Main Results:

  • The Random Forest Regressor model demonstrated the lowest localization error, measured by Average Euclidean Distance (AED) and Root-Mean-Square Error (RMSE).
  • Results indicate that 90% of RFID tags were localized within 1 meter of their true position.
  • Furthermore, 67% of tags were found to be within 50 cm of their actual location.

Conclusions:

  • Machine learning, particularly the Random Forest Regressor, significantly enhances the accuracy of indoor localization using UHF RFID systems.
  • The proposed method shows high precision, with a majority of tags localized within 50 cm, paving the way for reliable indoor tracking.