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Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2024, 10(4), pp.101-108
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : July 10, 2024
  • Accepted : August 9, 2024
  • Published : August 31, 2024

Harun Jamil 1 Naeem Iqbal 1 Murad Ali Khan 1 Syed Shehryar Ali Naqvi 1 Do Hyeun Kim 1

1제주대학교

Accredited

ABSTRACT

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications

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