[time]
2023-06-03T18:40:58+02:00

[track]
3

[team_name]
IOT2US

[team_institution]
Queen Mary, University of London

[logolink]


[team_members]


[reference_person]
Yonglei Fan; Qiqi Shu; Zhao Huang

[reference_email]
yonglei.fan@qmul.ac.uk

[description_short]
Dear Organizer:
 we will mainly use the acce, gyro and Mega to estimate the step length, step and direction.
it is real time indoor positioning method, we will separate all data into small pieces and use these pieces of data to do the positioning.
For the first positioning, we may use wifi, or Bluetooth data. 
Kalman filter will be used to minimize the error and the cumulative error. But there must be some cumulative error which is hard to deal with. we should think a better way to fix it.
Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long-term operation. To eliminate the accumulated errors in PDR localisation, we proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed , which recognizes the 


[description_long_link]
https://github.com/vanylFM/IPIN2022-Team-IOT2US

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true

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true

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