A Robust Localization Method for Unmanned Surface Vehicle (USV) Navigation Using Fuzzy Adaptive Kalman Filtering

Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles' development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a ra...

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Vydáno v:	IEEE access Ročník 7; s. 46071 - 46083
Hlavní autoři:	Liu, Wenwen, Liu, Yuanchang, Bucknall, Richard
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive algorithms Adaptive estimation Adaptive filters Adaptive systems Algorithms Autonomous navigation Data integration Data models fuzzy logic Heuristic algorithms Kalman filters Localization method multi-sensor data fusion Multisensor fusion Navigation Noise measurement Sensors Surface vehicles Unmanned vehicles Unscented Kalman Filter (UKF) USV navigation Vehicles
ISSN:	2169-3536, 2169-3536
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Abstract	Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles' development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a range of environments under varying dynamic constraints. This paper proposes a novel sensor data fusion algorithm employing an Unscented Kalman Filter (UKF) for the autonomous navigation of an Unmanned Surface Vehicle (USV). Since the navigational sensors on-board the USV are subject to operational uncertainties caused by equipment limitations and environmental disturbances, an improved UKF algorithm with the capability of adaptive estimation, namely fuzzy adaptive UKF data fusion algorithm, has been proposed to obtain reliable navigational information. The conventional UKF is capable of fusing a number of raw sensor measurements and generating relatively accurate estimations with proper a priori knowledge of system noise. To deal with systems that lack such information, a fuzzy adaptive estimation method is introduced to enhance the performance of the conventional UKF, making the algorithm capable of verifying and correcting the associated sensor noise in real time. The proposed fuzzy adaptive UKF data fusion algorithm has been tested and evaluated in different simulations modeled using practical maritime environments and the results are compared with the conventional UKF. The sensor measurements taken from a practical USV trial have also been applied to the proposed algorithm for further validation.
AbstractList	Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles' development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a range of environments under varying dynamic constraints. This paper proposes a novel sensor data fusion algorithm employing an Unscented Kalman Filter (UKF) for the autonomous navigation of an Unmanned Surface Vehicle (USV). Since the navigational sensors on-board the USV are subject to operational uncertainties caused by equipment limitations and environmental disturbances, an improved UKF algorithm with the capability of adaptive estimation, namely fuzzy adaptive UKF data fusion algorithm, has been proposed to obtain reliable navigational information. The conventional UKF is capable of fusing a number of raw sensor measurements and generating relatively accurate estimations with proper a priori knowledge of system noise. To deal with systems that lack such information, a fuzzy adaptive estimation method is introduced to enhance the performance of the conventional UKF, making the algorithm capable of verifying and correcting the associated sensor noise in real time. The proposed fuzzy adaptive UKF data fusion algorithm has been tested and evaluated in different simulations modeled using practical maritime environments and the results are compared with the conventional UKF. The sensor measurements taken from a practical USV trial have also been applied to the proposed algorithm for further validation.
Author	Liu, Yuanchang Liu, Wenwen Bucknall, Richard
Author_xml	– sequence: 1 givenname: Wenwen orcidid: 0000-0003-1082-4401 surname: Liu fullname: Liu, Wenwen email: w.liu.11@ucl.ac.uk organization: Department of Mechanical Engineering, University College London, London, U.K – sequence: 2 givenname: Yuanchang surname: Liu fullname: Liu, Yuanchang organization: Department of Mechanical Engineering, University College London, London, U.K – sequence: 3 givenname: Richard surname: Bucknall fullname: Bucknall, Richard organization: Department of Mechanical Engineering, University College London, London, U.K
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SubjectTerms	Adaptive algorithms Adaptive estimation Adaptive filters Adaptive systems Algorithms Autonomous navigation Data integration Data models fuzzy logic Heuristic algorithms Kalman filters Localization method multi-sensor data fusion Multisensor fusion Navigation Noise measurement Sensors Surface vehicles Unmanned vehicles Unscented Kalman Filter (UKF) USV navigation Vehicles
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Title	A Robust Localization Method for Unmanned Surface Vehicle (USV) Navigation Using Fuzzy Adaptive Kalman Filtering
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