Abstract
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.
Highlights
Alignment is the essential procedure for strapdown inertial navigation systems (INS)
We propose a two-dimensional hidden Markov model based Kalman filter (HMM-KF) to pre-process the inertial measurement unit (IMU) output signals, most of the error components referred above could be filtered out by the proposed Hidden Markov model (HMM)-KF, the useful data for INS alignment can be obtained
We use the standard Kalman filter to estimate the misalignment angles in the fine alignment procedure, the state and measurement equations of the Kalman filter are both established in the inertial frame
Summary
Alignment is the essential procedure for strapdown inertial navigation systems (INS). The primary problem of ship’s strapdown INS alignment under mooring condition is that the gravity and the earth rotational rate measurements will be disturbed by lineal and angular movements of the ship especially for the accelerations measurements, resulting in the alignment accuracy falling and time increasing [16,17,18]. The theory of HMM [51], we consider the useful IMU outputs for alignment as a HMM, viz., the valid measurements of IMU used for INS alignment (the local gravity and earth rotational rate) are hidden in the IMU’s raw outputs which include intrinsic sensor noise and external disturbance. We propose a two-dimensional hidden Markov model based Kalman filter (HMM-KF) to pre-process the IMU output signals, most of the error components referred above could be filtered out by the proposed HMM-KF, the useful data for INS alignment can be obtained.
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