A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions

Sensors

Feng Sun¹  Haiyu Lan¹  Chunyang Yu¹  Naser El-Sheimy²  Guangtao Zhou¹  Tong Cao¹ 

[1] Marine Navigation Research Institute, College of Automation, Harbin Engineering University, Harbin 150001, China; E-Mails:;Department of Geomatics Engineering, 2500 University Drive NW, University of Calgary, Calgary, AB T2N 1N4, Canada; E-Mail:

关键词: inertial navigation systems (INS);    alignment;    mooring condition;    inertial measurement unit (IMU);    inertial frame;    hidden Markov model;    Kalman filter;   

DOI  :  10.3390/s130708103

来源: mdpi

【 摘 要 】

Strapdown inertial navigation systems (INS) need an alignment process to determine the initial attitude matrix between the body frame and the navigation frame. The conventional alignment process is to compute the initial attitude matrix using the gravity and Earth rotational rate measurements. However, under mooring conditions, the inertial measurement unit (IMU) employed in a ship's strapdown INS often suffers from both the intrinsic sensor noise components and the external disturbance components caused by the motions of the sea waves and wind waves, so a rapid and precise alignment of a ship's strapdown INS without any auxiliary information is hard to achieve. A robust solution is given in this paper to solve this problem. The inertial frame based alignment method is utilized to adapt the mooring condition, most of the periodical low-frequency external disturbance components could be removed by the mathematical integration and averaging characteristic of this method. A novel prefilter named hidden Markov model based Kalman filter (HMM-KF) is proposed to remove the relatively high-frequency error components. Different from the digital filters, the HMM-KF barely cause time-delay problem. The turntable, mooring and sea experiments favorably validate the rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of HMM-KF.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.