Department of Aerospace Engineering, Seoul National University, Seoul 08826, Korea.
Sensors (Basel). 2020 Jul 22;20(15):4069. doi: 10.3390/s20154069.
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.
在系泊环境中，惯性导航系统（INS）的对准应考虑到波浪或风的运动。INS 的对准通过使用零速度作为测量值的扩展卡尔曼滤波器（EKF）来执行。然而，在系泊条件下，这不是完美的静止，因此测量误差协方差矩阵应该进行调整。此外，如果将测量误差协方差矩阵固定为一个值，则根据系泊条件的变化，对准时间可能会延长或性能可能会降低。为了解决这个问题，我们提出了一种使用自适应卡尔曼滤波器和基于卷积神经网络（CNN）的学习的对准方法。通过在系泊环境中的蒙特卡罗模拟，验证了所提出方法在对准时间和准确性方面的优越性。
