@article{ART003112319},
author={Harun Jamil and Naeem Iqbal and Murad Ali Khan and Syed Shehryar Ali Naqvi and Do Hyeun Kim},
title={Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2024},
volume={10},
number={4},
pages={101-108}
TY - JOUR
AU - Harun Jamil
AU - Naeem Iqbal
AU - Murad Ali Khan
AU - Syed Shehryar Ali Naqvi
AU - Do Hyeun Kim
TI - Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors
JO - Journal of Internet of Things and Convergence
PY - 2024
VL - 10
IS - 4
PB - The Korea Internet of Things Society
SP - 101
EP - 108
SN - 2466-0078
AB - 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
KW - IoT;Indoor localization;Pedestrian Dead Reckoning;Neural Network;motion recognition;and Smartphone Sensors
DO -
UR -
ER -
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi and Do Hyeun Kim. (2024). Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors. Journal of Internet of Things and Convergence, 10(4), 101-108.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi and Do Hyeun Kim. 2024, "Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors", Journal of Internet of Things and Convergence, vol.10, no.4 pp.101-108.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi, Do Hyeun Kim "Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors" Journal of Internet of Things and Convergence 10.4 pp.101-108 (2024) : 101.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi, Do Hyeun Kim. Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors. 2024; 10(4), 101-108.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi and Do Hyeun Kim. "Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors" Journal of Internet of Things and Convergence 10, no.4 (2024) : 101-108.
Harun Jamil; Naeem Iqbal; Murad Ali Khan; Syed Shehryar Ali Naqvi; Do Hyeun Kim. Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors. Journal of Internet of Things and Convergence, 10(4), 101-108.
Harun Jamil; Naeem Iqbal; Murad Ali Khan; Syed Shehryar Ali Naqvi; Do Hyeun Kim. Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors. Journal of Internet of Things and Convergence. 2024; 10(4) 101-108.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi, Do Hyeun Kim. Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors. 2024; 10(4), 101-108.
Harun Jamil, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi and Do Hyeun Kim. "Enhanced Indoor Localization Scheme Based on Pedestrian Dead Reckoning and Kalman Filter Fusion with Smartphone Sensors" Journal of Internet of Things and Convergence 10, no.4 (2024) : 101-108.
