基于ISAR成像的非合作航天器位姿识别网络

Non-cooperative Spacecraft Pose Recognition Network Based on ISAR Imaging

非合作航天器缺乏合作信息,无法直接利用传感器获得位姿数据,提出一种基于ISAR图像的位姿识别网络.相比于空间摄影卫星拍摄的图像以及仿真数据,该图像更易获取、成本更低,但存在分辨率低、面板成像不完整等问题.因此,该网络在图像预处理时,通过对YOLOX-tiny的调整,将其作为航天器裁剪网络,避免图像中标记的数据影响后续网络的训练,使网络仅关注航天器所在区域.利用增强的Lee滤波滤除图像噪声,提升图像的质量.在骨干网络中,加入STN模块,使网络选择最相关的区域注意,将U-Net网络设计成密集残差块结构并结合CBAM模块,减少下采样期间的特征损失,提高模型的准确性.此外,引入了多头自注意力来捕获更多的全局信息.实验结果表明,该模型最小、最大、平均误差较于目前的一些主流模型均有所提升,误差缩小了0.5-0.6,从而证明该网络具有更好的位姿识别能力.

Due to the lack of cooperative information,non-cooperative spacecraft cannot obtain pose data directly from sensors.Therefore,a pose recognition network based on inverse synthetic aperture radar(ISAR)images is proposed.Compared with the images taken by space photography satellites and simulation data,this kind of image is easier to obtain and cheaper,but there are some problems such as low resolution ratio and incomplete panel image.Therefore,in image preprocessing,the network uses YOLOX-tiny as a spacecraft clipping network by adjusting it to avoid the data marked in the image affecting the subsequent network training,so that the network only focuses on the region where the spacecraft is located.The enhanced Lee filter is used to remove image noise and improve image quality.In the backbone network,the STN module is added to make the network select the most relevant region attention,and the U-Net is designed into a dense residual block structure and combined with the CBAM module to reduce the feature loss during sampling and improve the accuracy of the model.In addition,multi-head self-attention is introduced to capture more global information.The experimental results show that the minimum,maximum,and average errors of this model are improved compared with some mainstream models,and the errors are reduced by 0.5-0.6.All this proves that the network has a better pose recognition ability.