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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Vydané v:IEEE access Ročník 7; s. 46071 - 46083
Hlavní autori: Liu, Wenwen, Liu, Yuanchang, Bucknall, Richard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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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Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2909151