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

Harun Jamil (하런자밀); Naeem Iqbal (나임 이크발); Murad Ali Khan (무라드 알리 칸); Syed Shehryar Ali Naqvi (시이드 세흐르야 알리 나크비); Do-Hyeun Kim (김도현)

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

KEYWORDS

IoT, Indoor localization, Pedestrian Dead Reckoning, Neural Network, motion recognition, and Smartphone Sensors

Citation status

* References for papers published after 2024 are currently being built.

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Journal of Internet of Things and Convergence 2024 KCI Impact Factor : 0.69