Abstract
Alignment of the inertial navigation system (INS) in the mooring environment should take into account the movements of the waves or wind. The alignment of the INS is performed through an extended Kalman filter (EKF) using zero velocity as a measurement. However, in the mooring condition, this is not perfect stationary, thus the measurement error covariance matrix should be adjusted. In addition, if the measurement error covariance matrix is fixed to one value, the alignment time may take longer or the performance may be reduced depending on the change in mooring conditions. To solve this problem, we propose an alignment method using adaptive Kalman filter and convolution neural network (CNN)-based learning. The proposed method was verified for the superiority of alignment time and accuracy through Monte Carlo simulation in a mooring environment.
Highlights
The inertial navigation system (INS) is the most fundamental navigation system that provides position, velocity, and attitude information using the gyro to measure angular velocity and the accelerometer to measure the linear acceleration of the vehicle
We propose adaptive extended Kalman filter (EKF) and learning-based EKF to perform alignment using a Kalman filter in the mooring environment
Since the size of the wave in the mooring environment continuously changes with time and position, the conventional alignment method using a fixed measurement error covariance matrix has limitations
Summary
The inertial navigation system (INS) is the most fundamental navigation system that provides position, velocity, and attitude information using the gyro to measure angular velocity and the accelerometer to measure the linear acceleration of the vehicle. A gyrocompassing method using a control law or an estimator based on a Kalman filter technique capable of estimating errors of sensors as well as an attitude during alignment has been applied. The stationary state has a complete zero velocity, the alignment process is performed using the zero-velocity information as a measurement in Kalman filter. The most crucial point is how to handle this linear velocity and rotation that repeatedly occur in the mooring environment with wind or wave. These movements appear as errors or disturbances that interfere with a stationary condition, EKF-based alignment can be performed by setting the measurement error covariance large. The CNN-based learning method learns the optimal measurement error covariance matrix to minimize the error in changing the mooring environment based on a large number of data
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