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Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model

Gunawan, BA; Liu, Y; Li, X; (2020) Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model. In: Proceedings of the 2020 International Conference on System Science and Engineering (ICSSE). IEEE: Kagawa, Japan. Green open access

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Abstract

Unscented Kalman Filter (UKF) remains to be a prevalent multi-sensor fusion method in many practices, including navigational tracking for Unmanned Surface Vehicles (USVs). This paper suggests that results from UKF fusion is unsatisfactory for USVs’ relatively smooth path due to UKF’s lack of versatility. Hence, it is proposed here that by replacing the UKF with Interacting Multiple Model (IMM), estimation results will better represent USV’s movement. Furthermore, this paper proposes slight modification to the IMM in order to heighten the algorithm’s confidence in switching modes. By exploiting angular velocity information from Inertial Measurement Unit (IMU), an independent mode probability can be obtained which is then injected into the IMM. Computer simulations based on maritime operations were done to show that the proposed IMU-based IMM is able to react faster to mode changes, giving more reliable outcomes.

Type: Proceedings paper
Title: Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model
Event: 2020 International Conference on System Science and Engineering (ICSSE)
Location: Kagawa, Japan, Japan
Dates: 31 August 2020 - 03 September 2020
ISBN-13: 978-1-7281-5960-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICSSE50014.2020.9219262
Publisher version: https://doi.org/10.1109/ICSSE50014.2020.9219262
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Interacting Multiple Model (IMM), adaptive estimation, USV navigation, GPS/IMU
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10112860
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