A robust self-alignment method for ship's strapdown INS under mooring conditions.
A robust self-alignment method for ship's strapdown INS under mooring conditions.
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DOI：
10.3390/s130708103
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发表时间：
2013-06-25
期刊：
影响因子：
--
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Sun F;Lan H;Yu C;El-Sheimy N;Zhou G;Cao T;Liu H
捷联惯导系统需要通过对准过程来确定载体坐标系和导航坐标系之间的初始姿态矩阵。传统的对准过程是利用重力和地球自转速率测量值计算初始姿态矩阵。然而，在系泊条件下，船用捷联惯导系统中的惯性测量单元（IMU）经常受到传感器固有噪声和海浪、风浪运动等外部干扰的影响，在没有任何辅助信息的情况下，很难实现捷联惯导系统的快速、精确对准。本文给出了一种鲁棒的解决方案来解决这个问题。采用惯性系对准方法来适应系泊条件，利用惯性系对准方法的数学积分和平均特性，可以去除大部分周期性低频外干扰分量。提出了一种新的预滤波器隐马尔可夫模型卡尔曼滤波器（HMM-KF），以去除相对高频的误差分量。与传统的数字滤波器不同，HMM-KF算法几乎不存在时延问题。转台、系泊和海上实验验证了所提出的自对准方法的快速性和准确性以及HMM-KF良好的降噪性能。
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.
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影响因子：
5.6
作者：
Ali, Jamshaid;Ushaq, Muhammad
通讯作者：
Ushaq, Muhammad
影响因子：
3.6
作者：
El-Sheimy, N;Nassar, S;Noureldin, A
通讯作者：
Noureldin, A
影响因子：
5.6
作者：
Ali, Jamshaid;Mirza, M. Rasheeq Ullah Baig
通讯作者：
Mirza, M. Rasheeq Ullah Baig
DOI：
10.3390/s101009252
发表时间：
2010
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Chiang KW;Chang HW
通讯作者：
Chang HW
影响因子：
5.6
作者：
Acharya, Arunasish;Sadhu, Smita;Ghoshal, T. K.
通讯作者：
Ghoshal, T. K.