A Dynamic Error Compensation Method by Fusing EMD and Neural Network for Shipborne Rotation Inertial Navigation

This paper proposes a novel dynamic modeling approach that fuses Empirical Mode Decomposition (EMD) with artificial neural networks for addressing the online calibration of rotational inertial navigation axis errors. Initially, EMD is employed to extract characteristic features from signals containing inertial navigation system axis errors. Subsequently, artificial neural networks are utilized for pattern learning, followed by error prediction using the iteratively trained models. The study implements BPNN, LSTM, and CNN models for experimental validation. Results demonstrate that this methodology exhibits excellent feature extraction and modeling capabilities for axis errors. Furthermore, the neural network incorporating the CNN model achieves superior prediction accuracy compared to other neural networks.