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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Main Author: Jayakusuma, Edo
Other Authors: Law Choi Look
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/150354
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Applications of electronics
spellingShingle Engineering::Electrical and electronic engineering::Applications of electronics
Jayakusuma, Edo
Positioning of UWB and IMU with error state Kalman filter
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.
author2 Law Choi Look
author_facet Law Choi Look
Jayakusuma, Edo
format Final Year Project
author Jayakusuma, Edo
author_sort Jayakusuma, Edo
title Positioning of UWB and IMU with error state Kalman filter
title_short 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_sort positioning of uwb and imu with error state kalman filter
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150354
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