亮星辅助下基于坐标转换的快速星图识别方法

数字天顶仪作为一种地面使用的星敏感器，主要用于高精度定位。为提高仪器的工作效率需要对星图识别的快速性进行研究。通过对恒星像点理论坐标与图像坐标的分析，构建坐标转换模型。依据星表中恒星的分布及星等筛选出视场范围内的亮星，并构建导航星表。结合导航星表完成3颗亮星的准确识别，在识别亮星的基础上解算坐标转换模型的参数。通过构建的坐标转换模型对视场范围内的恒星进行坐标转换，将转换后的星点坐标与提取的星点图像坐标进行匹配完成星图的识别，这样能够提高星图识别的快速性。实验数据表明：在保证识别星点数量的基础上，采用亮星辅助下基于坐标转换的星图识别方法使时间缩短为改进三角形星图识别算法的五分之一。     

一种改进的三角形星图识别方法

首先提出在直角坐标系下将天球球面均匀地分成486个分区的方法——"内接正方体法",实现导航星的快速检索。然后按照星对角距的大小升序排列,直接存储星对角距,并通过设置星对角距状态标识,完成星对角距的快速匹配搜索。最后,引入验证识别环节有效地解决观测三角形与匹配的导航三角形产生的冗余问题。仿真实验表明,改进的三角形星图识别方法对星点位置噪声和星等(亮度)噪声均具有较强的抗干扰能力,且在识别时间和存储空间的需求方面均比传统的三角形算法也具有较大的优势。     

一种改进的快速全天星图识别算法

针对三角形算法中模式识别率低、易造成误识别的问题,提出了一种改进的三角形算法。 首先,该算法通过对全天区导航星进行筛选,缩小星库存储空间;同时,在识别过程中对找到的三 边角距集合采用设置状态标识的方法,加快对探测星三角形的识别速度;在三角形匹配识别结果 不唯一时,引入其他观测星进行验证,确定唯一的匹配对应;在识别结束后,应用投影验证检验识 别结果。仿真结果表明,该算法能够有效地节省存储空间,缩短全天星图识别时间,与传统的三角 形算法相比具有较大的优势。     

一种结合角距特征的改进栅格星图识别算法

针对栅格算法易受邻域星点影响导致误匹配的问题,提出了一种结合角距特征的改进栅格星图识别算法。首先介绍了改进栅格算法的原理,其次设计了融合栅格识别模式和星角距识别模式的算法实现流程,最后开展了基于不同视角下的大视场仿真星图的算法试验验证和性能分析。结果表明,由于角距特征具有旋转不变性和不易受观测星邻域星点分布影响等特点,结合角距特征对失效观测星进行再匹配的改进栅格算法,在兼顾存储量需求小、运行速度快等优势的同时,识别率和鲁棒性也得到了提升,最高识别率可达98.88%,在位置噪声干扰以及缺失星干扰下,改进算法的识别率仍可保持在95%,说明算法鲁棒性强,具有较好的应用前景。     

基于帧间角距匹配的跟踪模式星图识别

为了提高星敏感器的跟踪匹配速率,提高星敏感器姿态更新率,本文提出了一种基于帧间角距匹配的跟踪模式星图识别方法.该方法充分利用上一时刻的角距匹配信息构建了一个实时跟踪星库,并把当前时刻的角距信息在实时跟踪星库中进行匹配识别,识别成功后利用识别时用到的信息对实时跟踪星库进行更新.仿真实验表明,该方法具有匹配时间短、匹配成功率高的优点.     

一种基于星座匹配的全天自主星图识别算法

讨论了一种新的无须任何先验知识的星图识别算法。通过对导航星座数据库进行调整和最大限度地利用了已识别基元所提供的匹配信息,使得算法的计算量和存储容量与传统的三角形匹配算法相当。ＭｏｎｔｅＣａｒｌｏ 模拟表明,在星等误差０．５ 星等（３σ）,位置误差１０ 角秒（１σ） 时,算法的识别成功率接近１００％ 。     

一种改进的星图降噪算法研究

在星敏感器的使用过程中,由于外界环境的影响及传感器自身的限制,拍摄出来的星图不可避免地存在一些噪声,因此对星图进行去噪处理是一项非常重要的工作。针对传统高斯模板滤波存在的引入邻域噪声、无法自行根据星图特性修正等造成去噪效果不好的问题,提出了一种改进的星图降噪算法。该方法在滤波前先进行坏点剔除工作,并采用高斯低通滤波与高通滤波结合的方式对图像进行处理,在抑制噪声的同时有效地保留了星点信号。通过阐述星敏感器的工作原理,分析星图的噪声特性,对星图滤波去噪算法进行研究,并进行模拟星图影像提取星点坐标实验。结果表明：使用该算法进行滤波比传统的高斯滤波算法提取的质心坐标精确度更高,较传统方法横坐标提高0.00538个像素点,纵坐标提高0.0077个像素点,证实了图像处理算法的有效性。     

寡星条件下的半球谐振陀螺与星敏感器 组合姿态测量算法

针对半球谐振陀螺与星敏感器松组合系统在寡星条件下无法正常工作的问题,采用新的星图识别算法和新的数据融合观测方程,使星敏感器在观测到的导航星数量为1或2颗的情况下完成星图识别,从而能够完成组合测姿。     

P向量实现快速星图识别的方法

 提出了一种快速的星图识别方法。该方法识别时提取出描述观测三角形的一维特征量P,根据该值快速检索到对应的导航三角形,减少了计算过程。由于获得星点位置的时候存在误差,所以将一定误差范围内的三角形均作为待选三角形考虑。上述识别后引入了验证识别环节,保证识别的准确性。随机实验统计表明,同等星点位置噪声方差下,该方法和栅格算法相比识别率提高约1%,识别时间约为后者的1/5。     

基于QTM的导航星库划分方法

为了提高星敏感器观测星的检索速度,提出了一种基于球面四元三角网(Quaternary Triangular Mesh,QTM)的导航星库划分方法.该方法首先利用天球的内接正八面体把全天球划分成8个子天区,然后在每个子天区中进行与经纬度相关的剖分和编码,最后扫描恒星星表,把每颗导航星信息划归到相应的子天区内存储.利用此星库进行的局部天球星图识别仿真实验表明该星库具有很高的检索效率.     

Star recognition algorithm for APS star tracker: oriented triangles

An efficient star pattern recognition algorithm is presented. The purpose of this algorithm is to make sure of the compatibility of the software and the imaging sensor noise level. The new CMOS APS sensors have not currently reached the same accuracy as the former CCD sensors in position as well as in magnitude determination, especially in the dynamic stages. This algorithm allows the system to recognize the star pattern 20% faster than with reference algorithms. No false recognition has been noticed. Used databases have a size 5 to 10 times smaller, depending on other reference algorithms. Oriented triangles are used to compare the measured star pattern with the catalogue stars. The triangle's characterization criteria propose several solutions in a first time. A unique solution is selected by means of identification and validation methods in a second time. First results, presented hereinafter, are very encouraging, and this algorithm may be used in the future APS star trackers. APS star tracker robustness is significantly enhanced by this method during the critical navigation phases     

Recursive mode star identification algorithms

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.     

Geometric voting algorithm for star trackers

We present an algorithm for recovering the orientation (attitude) of a satellite-based camera. The algorithm matches stars in an image taken with the camera to stars in a star catalogue. The algorithm is based on a geometric voting scheme in which a pair of stars in the catalogue votes for a pair of stars in the image if the angular distance between the stars of both pairs is similar. As angular distance is a symmetric relationship, each of the two catalogue stars votes for each of the image stars. The identity of each star in the image is set to the identity of the catalogue star that cast the most votes. Once the identity of the stars is determined, the attitude of the camera is computed using a quaternion-based method. We further present a fast tracking algorithm that estimates the attitude for subsequent images after the first algorithm has terminated successfully. Our method runs in comparable speed to state of the art algorithms but is still more robust than them. The system has been implemented and tested on simulated data and on real sky images.     

Rapid Star Tracking Algorithm for Star Sensor

A rapid star tracking algorithm is proposed. In order to speed up the tracking, three techniques includng parallel star centroiding, sorting, and star catalogue partition are designed for three time-consuming portions in tracking algorithms. The parallel star centroiding is implemented with Field Programmable Gate Array (FPGA) which avoid image storage and transmission. Update rate of star sensor is improved. Sorting star coordinates in star image plane, and then matching, which avoid matching between stars with a long distance in image plane. Star catalogue partition divides the celestial sphere into many small partitions. In star mapping, guide stars are searched in the partitions near the direction of star sensor's boresight is not in the whole celestial sphere and therefore reduced the total number of searched guide stars. The software and hardware performance of tracking algorithms are simulated. Tracking robustness and the tracking speed comparison are tested in the software simulation. In hardware tests, the tracking time in every step is obtained.     

发展单兵星敏导航装备的必要性及技术特点

星光导航不受电磁干扰、能提供绝对姿态信息，具有重大战略意义。目前的星敏主要是为空天基导航而发展的，将其应用于单兵导航是否必要、需克服哪些技术难点、能达到怎样的效果等，都是值得探索的问题。为此，首先论述了发展单兵星敏导航装备的必要性，包括：不受电磁干扰破坏，是其他单兵导航装备失效时的保底装备；定向精度高，能弥补单兵卫星导航装备无法定向的缺陷；已突破部分气象因素限制，具备了全天时导航条件等。然后探讨了单兵星敏导航与空天基星敏导航、卫星导航、天文大地测量等比较所具有的不同技术特点。提出了发展单兵星敏导航装备需解决的关键技术问题，包括：气象因素影响的消减、时间基准和电源的长期维持、系统探测性能与系统小型化的平衡等。研究结果可为发展单兵星敏导航技术、设计研制单兵星敏导航系统提供参考。     

地球卫星星光折射导航量测量及其性能对比

星光折射天文导航是一种重要的地球卫星自主导航方式,量测量是影响其导航精度的重要因素。在地球卫星星光折射导航中,折射视高度、星光折射角、折射星像素坐标（折射星矢量）是3种常用的量测量,结合星光折射导航的基本原理重点介绍了这3种量测量的获取方法和量测模型,通过仿真和可观性分析比较了相同条件下3种量测量的导航性能。仿真结果表明,由于折射星像素坐标可以同时反映星光折射的大小和方向可观性高,而星光折射角和折射视高度仅能反映星光折射的大小,无法反映其方向可观性低,因此折射星像素坐标的导航性能优于星光折射角和折射视高度。此外,本文也对星敏感器精度、卫星轨道高度、星敏感器安装夹角3种因素对3种方法导航性能的影响进行了分析。