[time]
2023-05-29T15:05:37+02:00

[track]
3

[team_name]
IPNL

[team_institution]
The Hong Kong Polytechnic University

[logolink]


[team_members]


[reference_person]
Qing Liang

[reference_email]
aae-qing.liang@polyu.edu.hk

[description_short]
The competing system follows the particle filter-based multi-sensor fusion positioning framework using PDR, WiFi RSSI fingerprinting, and map matching. As is well known, the AHRS heading estimation accuracy is critical to good PDR performance. Here we use the magnetometer alongside IMU for accurate attitude and low-drift heading estimation. In particular, quasi-static magnetic fields are detected to correct gyroscope biases and reduce heading drift. Opportunistic magnetic headings that are unaffected by magnetic disturbances are identified to provide occasional absolute heading correction.

    PDR entails step detection, step length estimation, and heading change estimation. Step events are detected on the vertical acceleration by examing the periodic human motion patterns. We use an adaptive zero-crossing detector and finite-state machine for reliable step detection. We choose the Weinberg step length model as it is easy to tune with only one parameter. To deal with the pho

[description_long_link]
https://1drv.ms/b/s!Au9Ow143J8QEpUq6fPZK_t4kuwoo?e=cspSZK

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