针对捷联式海空重力仪开展初始误差补偿研究, 重力仪开机后其数据存在一个逐渐稳定的过程, 估计出初始段的不稳定值与稳定值间的误差, 实现重力数据误差补偿。该文提出了长短期记忆网络法, 该方法可通过不断学习现有数据后高精度地预测未来数据变化。经过实测重力测量数据验证了该方法具有很好的误差补偿效果, 提高了重力测量数据的准确性和有效性。实验结果表明, 对于重力数据初始段测量误差抑制效果超过90%, 对于捷联式海空重力仪作业运行效率具有重要价值。

In this paper, the research on the initial error compensation of strapdown sea-air gravimeter is carried out. After the gravimeter is turned on, the gravimeter data has a gradually stable process. The error between the unstable value and the stable value in the initial section is estimated, and the error compensation of gravity data is realized. In this paper, the long-term and short-term memory network method is proposed. This method can continuously learn the existing data and predict the future data changes with high accuracy. It is verified that by the measured gravity measurement data that the proposed method has a good error compensation effect, which improves the accuracy and effectiveness of gravity measurement data. The experimental results show that the effect of the measurement error suppression for the initial section of the gravity data is more than 90%, which is of great value for the operation efficiency of the strapdown sea-air gravimeter.