基于改进遗传算法的星图畸变校正方法

由于光学成像系统的非线性几何畸变,使得星敏感器所获星图与理想星图有一定的差别,为完成高精度定姿,必须对畸变星图进行校正.首先,介绍了光学系统的畸变原理,并建立了以径向几何畸变为主的星图畸变模型.之后引入改进遗传算法对畸变参数进行了优化:采用引进成长算子的二进制编码,避免了算法陷入伪极值;通过改进适应度函数和适时调整变异...     

星敏感器光学系统参数的确定

光学系统是星敏感器的重要组成部分。选择合理的参数不但能确保星敏感器达到预期的性能，而且有助于降低产品的研制成本。本文介绍一种确定光学系统参数的新方法，包括通过参考比较已有星敏感器及其光学系统特性来选取新光学系统焦距、相对孔径和视场；根据所选取的参数预计星敏感器的捕获概率；采用综合恒星光谱方法确定光学系统中心波长和光谱范围。     

星敏感器杂光抑制分析

介绍了用蒙特卡洛法进行星敏感器杂光分析的基本思想、数学模型以及表面热辐射概率模型 ,对典型卫星星敏感器的杂光传递进行了模拟。在复杂的杂光环境下 ,建立杂光传递模型 ,获得了星敏感器杂光环境的定量杂光贡献 ,并对大衰减比遮光罩及光学系统内的杂光传递进行了跟踪 ,给出了遮光罩的消杂光能力     

一种基于Star算法的多路视频稳像方法研究

目前,基于特征点匹配的稳像算法中存在特征点提取速度慢、误匹配率高,以及多目相机的多路视频稳像太过耗时、实时性不好的问题,提出了一种基于Star特征点提取算法的多路视频快速稳像方法。首先采用Star算法提取视频当前帧特征点,然后采用光流法结合当前帧特征点信息跟踪预测下一帧对应的特征点,以加快特征点提取速度,同时减少所提取的特征点错误匹配率。通过对匹配的特征点求取相应的仿射变换矩阵获取帧间运动矢量,进而采用卡尔曼滤波器对帧间运动矢量进行滤波处理,以滤除其中的相机随机抖动矢量并保留相机的主观运动矢量。最后依据滤波后的运动矢量进行图像补偿得到稳定的图像序列。针对复眼导引头存在的多路视频抖动情况,基于OpenMP并行开发库,利用多核处理器的优势,实现了多路抖动视频的并行实时稳像。所设计的数字稳像算法在PC平台下对4路抖动视频（OTB100的BlurCar系列视频,分辨率为640×480,帧率为30 FPS）并行稳像的结果参数为：峰值信噪比（Peak Signal-to-Noise Ratio,PPSNR）平均提升了4.62 dB,单帧耗时平均为14.51 ms。经仿真实验证实,算法的计算效率和特...     

一种基于FPGA实现实时提取恒星星像坐标的方法

提出一种基于FPGA实现实时提取恒星星像坐标的方法。该方法在提取恒星星像坐标过程中,不必利用当前图像下一列坐标的灰度值和当前列下一行坐标的灰度值,而仅仅采用当前图像当前行的上一列坐标的灰度值以及当前列的上一行坐标的灰度值, A/D转换成数字量后,把星图图像保存到SRAM和提取恒星星像坐标两个过程并行。实验结果表明,采用该方法不仅能够准确提取恒星星像坐标,而且能够进一步提高坐标提取的实时性,满足星敏感器并行流水工作模式的要求。     

星载光学敏感器受天体干扰的可能性分析

各种用途的星载光学敏感器均可能受到日、月、地等天体进入敏感区所形成的干扰.对光轴固定于星体的星敏感器,以及光轴在空间中保持方位不变的天文望远镜两类星载光学敏感器,根据敏感锥面和天体锥面之间的空间位置关系,导出了天体干扰的发生条件,给出了预测干扰发生时间的实用计算公式.      

视场内导航星分布的预测

给出理想情况下视场内导航星分布模型的基础上,根据敏感器的星等门限和测量精度,进一步实现了存在星等误差情况下导航星分布的预测,使得该模型更为接近真实的情况.实验表明,采用该模型获得的分布与用MonteCarlo方法获得的统计分布极为接近,证明了该模型的正确性和有效性,从而得到了一种事先对视场内导航星分布作出较为准确估计的新的方法.      

一种采用行列聚类并行的星像坐标提取方法

为了提高星敏感器的数据更新率,必须减少星像坐标的提取时间.在提取星像坐标的过程中,现有基于FPGA的方法虽然避免了星图保存和读取过程,然而需要高性能的FPGA才能实现.为此提出了一种采用行列聚类并行的星像坐标提取方法,将逐个像素聚类的方式优化成像素队列聚类的方式,实现了对恒星星像坐标的实时提取,可以解决现有的实时提取星...     

GOCE star tracker attitude quaternion calibration and combination

We analyse the inter-boresight angles (IBA) measured by the star trackers on board the GOCE satellite and find that they exhibit small offsets of 7–9″ with respect to the ones calculated from the rotation of the star tracker reference frames to the satellite reference frame. Further, we find small variations in the offsets with a peak-to-peak amplitude of up to 8″, which correlate with variations of the star trackers’ temperatures. Motivated by these findings, we present a method for combining the attitude quaternions measured by two or more star trackers that includes an estimation of relative attitude offsets between star trackers as a linear function of temperature. The method was used to correct and combine the star tracker attitude quaternions within the reprocessing of GOCE data performed in 2018. We demonstrate that the IBA calculated from the corrected star tracker attitude quaternions show no significant offsets with respect to the reference frame information. Finally, we show that neglecting the star tracker attitude offsets in the processing would result in perturbations in the gravity gradients that are visible at frequencies below 2?mHz and have a magnitude of up to 90?mE. The presented method avoids such perturbations to a large extent.     

A triangle voting algorithm based on double feature constraints for star sensors

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.