Abstract
Marine seismic exploration is an important part of offshore oil and gas exploration, which requires accurate attitude information of submarine towing equipment. Conventional attitude solution algorithm or Kalman filter algorithm cannot satisfy the current requirements of high accuracy, high reliability, strong environmental adaptability and low cost. In view of the low accuracy and poor environmental adaptability of the traditional Kalman filter algorithm, this paper proposes a CNN-EKF fusion attitude calculation algorithm based on the study of the extended Kalman filter (EKF) model and the convolutional neural network (CNN) model. The system noise variance matrix (Q) and the observation noise variance matrix(R)of EKF were optimized by CNN, and the final solution results were obtained. Compared the traditional Kalman filtering model with the CNN-EKF fusion filtering model, experimental results shows that the algorithm improves the accuracy of attitude calculation and enhances the adaptive ability to the environment.
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Acknowledgements
The research was supported by the National Key Research and Development Program of China (Grant No. 2016YFC0303901), Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (ZJW-2019-04).
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Zheng, B., Yang, X., Hu, H. (2021). Research on Attitude Solving Algorithm of Towing Cable Based on Convolutional Neural Network Fusion Extended Kalman Filter. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_20
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DOI: https://doi.org/10.1007/978-981-16-5940-9_20
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