Journal
IEEE ACCESS
Volume 4, Issue -, Pages 5695-5709Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2606408
Keywords
Underwater glider; inertial sensor; weight self-adjustment; attitude estimation
Categories
Funding
- Public Science and Technology Research Funds Projects of Ocean [201205035]
- Natural Science Foundation of Jiangsu Province [BK20160699]
- Fundamental Research Funds for the Central Universities [2242016R20024]
- National Natural Science Foundation of China [51375087, 51405203]
- Voyage of Scientific Investigation and Experimental Study of Taiwan Strait by NSFC [41449904]
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High-accuracy attitude estimation plays an important role in gliding with long endurance for an underwater glider. Because microelectromechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption, they are used as main sensors to determine navigation information. However, in the complicated and harsh underwater environment, the performances of MEMS sensors degrade and errors will become larger. Moreover, acceleration or deceleration while gliders going up and down, sudden vibration of gliders due to inevitable disturbances will bring larger errors for sensors. So it is difficult to acquire the high accuracy attitude calculated by inertial measurement unit. In order to solve the above problem, first, a novel weight self-adjustment extended Kalman filtering method, which can adjust the weight autonomously through estimating adaptively measurement noise, is proposed to perform the optimal error estimation. Moreover, a fusion method that integrates the Adams implicit formula with the weight self-adjustment filtering method is proposed to achieve the more improvement in attitude estimation accuracy. The performance of this proposed algorithm is evaluated by the theoretical proofs and simulations. Subsequently, it is tested by the ship experiments and the lake trials. The results show that this proposed algorithm has a better performance in terms of attitude estimation accuracy than extended Kalman filtering (EKF)-only and self-adjustment EKF in this paper. Meanwhile, this algorithm has good robustness for attitude calculation even though pitch angle changes large.
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