Positioning of UWB and IMU with error state Kalman filter
Indoor-positioning has proved to be an important problem in recent years, because of the increasing urban environment complexity and unavailability of GPS indoors. Recently Ultra Wideband (UWB) have proved to be an emerging promising technology to solve this problem. As Ultra Wideband (UWB) have...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/150354 |
| _version_ | 1826114245905874944 |
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| author | Jayakusuma, Edo |
| author2 | Law Choi Look |
| author_facet | Law Choi Look Jayakusuma, Edo |
| author_sort | Jayakusuma, Edo |
| collection | NTU |
| description | Indoor-positioning has proved to be an important problem in recent years, because of
the increasing urban environment complexity and unavailability of GPS indoors. Recently
Ultra Wideband (UWB) have proved to be an emerging promising technology to solve this
problem. As Ultra Wideband (UWB) have high latency, previous work have incorporated
Inertial Measurement Unit (IMU) sensor fusion to mitigate this shortcoming.
Most
popular fusion algorithm to use is based on Extended Kalman Filter, and Unscented
Kalman Filter. However, long duration of usage will have some reduced precision, as there
are bias drifts for accelerometer and gyroscope measurement.
A recent paper by Liu Et Al. is addressing this problem. Which uses Error State
Kalman Filter to do online bias estimation of the IMU. Other than that, tightly coupled
attitude determination is available with ESKF, with no need to rely on other algorithms
like Mahony Filter for example.
The author wants to reenact the experiment by Liu et al. to demonstrate the feasibility
of such methods. |
| first_indexed | 2024-10-01T03:36:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/150354 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:36:21Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1503542023-07-07T18:20:28Z Positioning of UWB and IMU with error state Kalman filter Jayakusuma, Edo Law Choi Look School of Electrical and Electronic Engineering ECLLAW@ntu.edu.sg Engineering::Electrical and electronic engineering::Applications of electronics Indoor-positioning has proved to be an important problem in recent years, because of the increasing urban environment complexity and unavailability of GPS indoors. Recently Ultra Wideband (UWB) have proved to be an emerging promising technology to solve this problem. As Ultra Wideband (UWB) have high latency, previous work have incorporated Inertial Measurement Unit (IMU) sensor fusion to mitigate this shortcoming. Most popular fusion algorithm to use is based on Extended Kalman Filter, and Unscented Kalman Filter. However, long duration of usage will have some reduced precision, as there are bias drifts for accelerometer and gyroscope measurement. A recent paper by Liu Et Al. is addressing this problem. Which uses Error State Kalman Filter to do online bias estimation of the IMU. Other than that, tightly coupled attitude determination is available with ESKF, with no need to rely on other algorithms like Mahony Filter for example. The author wants to reenact the experiment by Liu et al. to demonstrate the feasibility of such methods. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-13T13:16:32Z 2021-06-13T13:16:32Z 2021 Final Year Project (FYP) Jayakusuma, E. (2021). Positioning of UWB and IMU with error state Kalman filter. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150354 https://hdl.handle.net/10356/150354 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Applications of electronics Jayakusuma, Edo Positioning of UWB and IMU with error state Kalman filter |
| title | Positioning of UWB and IMU with error state Kalman filter |
| title_full | Positioning of UWB and IMU with error state Kalman filter |
| title_fullStr | Positioning of UWB and IMU with error state Kalman filter |
| title_full_unstemmed | Positioning of UWB and IMU with error state Kalman filter |
| title_short | Positioning of UWB and IMU with error state Kalman filter |
| title_sort | positioning of uwb and imu with error state kalman filter |
| topic | Engineering::Electrical and electronic engineering::Applications of electronics |
| url | https://hdl.handle.net/10356/150354 |
| work_keys_str_mv | AT jayakusumaedo positioningofuwbandimuwitherrorstatekalmanfilter |