基于成像特性与回归网络的卫星目标姿态估计  

Attitude estimation of satellite targets based on imaging characteristics and regression network

作　　者：范磊[1] 王宏强[1] 杨琪 曾旸 邓彬[1] FAN Lei;WANG Hongqiang;YANG Qi;ZENG Yang;DENG Bin(College of Electronic Science and Technology,National University of Defense Technology,Changsha Hunan 410073,China)

机构地区：[1]国防科技大学电子科学学院,湖南长沙410073

出　　处：《太赫兹科学与电子信息学报》2023年第4期572-577,共6页Journal of Terahertz Science and Electronic Information Technology

基　　金：国家自然科学基金资助项目(61921001,61971427,62035014,62201591);国家重点研发计划(2022YFB3902400,2018YFB2202500)。

摘　　要：基于逆合成孔径雷达(ISAR)图像序列的卫星目标姿态估计是一项具有重大意义且富有挑战性的任务。现有的估计方法通常是基于图像中关键角点或线性部件的提取,较难满足实时需求,且都未能充分利用目标成像特性先验。本文提出一种基于成像特性与回归网络的卫星目标姿态估计方法:提前确定各种姿态下的卫星目标成像特性,并作为后续数据集标注的理论基础;区别于传统的分类问题,建立一种适用于姿态估计的回归网络与估计框架。采用毫米波频段的电测仿真计算数据对所提方法进行验证,结果表明,单张图像中估计的平均姿态误差可以控制在3.5°以内。Attitude estimation of satellite targets with the Inverse Synthetic Aperture Radar(ISAR)image sequences is a significant but challenging task.Existing estimation methods are normally focused on the extraction of critical corners or linear components from the image,which are hard to meet the real-time requirement and insufficient to exploit the prior of imaging characteristics.This paper presents a method for estimating the attitude of satellite targets based on imaging characteristics and regression networks.The imaging characteristics of satellite targets under various attitudes are firstly determined in advance and serve as the theoretical basis for subsequent dataset annotation.Thus,different from the traditional classification problem,a regression network and an estimation framework suited for attitude estimation are established.Finally,electro-magnetic simulation in millimeter frequency is carried out to validate that the proposed method can control the average attitude estimation error within 3.5°.

关 键 词：逆合成孔径雷达 成像特性 回归网络 姿态估计 实时预估 

分 类 号：TN957.52[电子电信—信号与信息处理]