[time]
2020-11-30T11:05:30+01:00

[team_name]
TJU

[team_institution]
School of Microelectronics, Tianjin University

[logolink]


[system_name]


[website]


[track]
3

[reference_person]
Liqiang Zhang, Boxuan Chen, Hu Li, Yazhen liao, Qingyuan Gong and Yu Liu

[email]
zhangliqiang@tju.edu.cn

[description]
A smartphone integrates rich sensors, including inertial sensor, magnetic sensor, light sensor, sound sensor and proximity sensor, etc. Therefore, it provides a possibility to localize pedestrians, which is valuable and challenging. In this competition, step-length and heading system (SHS) is used as our fundamental system. Acceleration information from inertial sensor is used to detect steps. Then the step length can be computed. To our best knowledge, step-length computing is accurate when a pedestrian moves forward on a level ground. However, the estimated step length becomes inaccurate when a pedestrian moves on a slope, such as walking upstairs or downstairs. To address the problem, we propose adaptive step-length estimation method aided by learning-based motion classifier. Since the performance of the SHS degrades with time and walking distance, reference positions should be introduced to modify the positioning error of the system. Therefore, we establish magnetic field and Wi-Fi-based fingerprint databases with the training and validation data. Then a neural network (NN) model is trained to predict the pre-given ground truth positions using geomagnetic information and Wi-Fi received signal strength. Finally, Kalman filter is leveraged to fuse the positioning results from the NN model and the SHS.

[references]