• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.521-534
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• 2021

Purpose: In this paper, we propose an indoor positioning scheme based on pedestrian dead reckoning using inertial measurement unit. By minimizing the effects of the orientation error of smart-phone, the more accurate estimation for the direction, the step count, and the stride can be achieved. Method: The effectiveness and the performance of the proposed scheme is evaluated by experiments, and it is compared with the conventional scheme in the same conditions. Result: The results showed that the positioning error of the proposed scheme was 0.76m, while that of the conventional scheme was 1.84m. Conclusion: Sine most people carry his/her own smart-phone, the proposed scheme can be helpful to recognize where he/she was and was heading when the fast evacuation is needed in indoors.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.302-313
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• 2018

As the demand for indoor location recognition system has been rapidly increased in accordance with the increasing use of smart devices and the increasing use of augmented reality, indoor positioning systems(IPS) using BLE (Bluetooth Lower Energy) beacons and UWB (Ultra Wide Band) have been developed. In this paper, a positioning plate is generated by using trilateration technique based on BLE Beacon and using RSSI (Received Signal Strength Indicator). The resultant value is used to calculate the PDR-based coordinates using the positioning element of the Inertial Measurement Unit sensor, We propose a precise indoor positioning algorithm that combines RSSI and PDR technique. Based on the plate algorithm proposed in this paper, the experiment have done at large scale indoor sports arena and airport, and the results were successfully verified by 65% accuracy improvement with average 2.2m error.