基于时变网格的对地观测卫星搜索海上船舶方法

Vessel search method by earth observation satellite based on time-varying grid

为提高对地观测卫星搜索海上船舶效能,提出了一种根据卫星对地观测幅宽和船舶最大航速划分区域时空维度的时变网格模型,将船舶航迹预测问题转化为时变网格转移概率预测问题,有效降低了多步预测的复杂度。改进了序列到序列(Seq2Seq)模型,通过对搜索区域内大量历史航迹的学习,实现了较高精确度的多步时变网格转移预测。设计了面向时变网格的卫星观测任务规划算法,以实际AIS数据和卫星信息开展了仿真实验,实验结果表明:基于深度学习的Seq2Seq模型多步预测时变网格具有较高的精确度,有效提升了对地观测卫星搜索海上船舶的效能。

In order to improve the effectiveness,a time-varying grid model is proposed to divide the spatial and temporal dimensions of the region according to the satellite earth observation width and the maximum vessel speed. The vessel track prediction problem is transformed into a time-varying grid transition probability prediction problem,which effectively reduces the complexity of multi-step prediction. The multi-step time-varying grid transfer prediction with high accuracy is realized by the improved sequence to sequence(Seq2Seq)model,which has learned a large number of historical tracks in the research area. A satellite observation task planning algorithm for time-varying grids is designed and simulation experiments are carried out based on actual AIS data and satellite information. The results of experiments show that the Seq2Seq model based on deep learning has high accuracy in multi-step prediction of time-varying grids,which effectively improves the effectiveness of vessel search by earth observation satellites.