基于深度网络的多源卫星数据舰船目标融合跟踪OA

With the rapid development of satellite remote sensing technology,a single data source no longer meets the needs of ship target tracking.The fusion of multi-source satellite observation data can provide comprehensive and accurate Earth observation information,overcome the limitations of a single data source,improve the target tracking performance,and thus support accurate analysis and decision-making.In this paper,the observation data from space-based microwave radar,electronic reconnaissance satellites,and synthetic aperture radar(SAR)satellites are adopted to study how to effectively fuse data from multiple satellite payloads to achieve accurate tracking of ship targets.First,a data fusion method based on the convolutional neural network(CNN)and attention mechanism is proposed,which can effectively integrate data from different modalities to enhance the performance of the model in complex tasks.Then,a data association algorithm based on graph neural networks(GNNs)is proposed,which ensures the consistency and continuity of each target during the tracking process.Simulation validation is carried out with the simulated dataset generated by the ship automatic identification system.The results show that the method obtains good fusion accuracy and tracking stability in three ship distribution density scenarios of 5 km×5 km,10 km×10 km,and 20 km×20 km,and has high value for engineering applications.