Staring-imaging satellite pointing estimation based on sequential ISAR images
作者机构：National Key Laboratory of Automatic Target RecognitionNational University of Defense TechnologyChangsha 410073China Xi’an Satellite Control CenterXi’an 710600China
出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)
年 卷 期：2024年第37卷第8期
页 面：261-276页
核心收录：
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：national foundation project of China national foundation
主 题：Staring-imaging Agile satellite Inverse Synthetic Aperture Radar(ISAR) Pointing estimation Cross-range scaling
摘 要：Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and ***,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation ***,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this *** address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique ***,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted *** on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this ***,the pointing of targets and the cross-range scaling factors of ISAR images are *** experiments based on actual satellite orbital parameters verify the effectiveness of the proposed *** work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.
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