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A Brightness-Referenced Star Identification Algorithm for APS Star Trackers

机译:APS星跟踪器的亮度参考恒星识别算法

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Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4∼5 times that of the pyramid method and 35∼37 times that of the geometric method.
机译:星际追踪器是目前最精确的航天器姿态传感器。结果,它们被广泛用于遥感卫星。由于传统的基于电荷耦合器件(CCD)的恒星跟踪仪具有有限的灵敏度范围和动态范围,因此恒星跟踪仪的匹配过程通常对恒星亮度不太敏感。对于有源像素传感器(APS)恒星跟踪仪,成像恒星的强度是可以在恒星识别过程中使用的有价值的信息。本文提出了一种改进的亮度参考恒星识别算法。该算法利用k矢量搜索理论,增加了成像星的强度来缩小搜索范围,从而提高了匹配过程的效率。根据不同的成像条件(回转,明亮的物体等),开发的匹配算法以两种识别模式之一运行:三星级模式和四星级模式。如果出现参考明亮的恒星(亮度大于三度的恒星),则该算法将运行三星级模式,从而进一步提高效率。将该方法与其他两种独特的方法金字塔和几何投票方法进行了比较。这三种方法均使用来自ZY-3 APS星跟踪仪的模拟数据和实际在轨数据进行了测试。使用由1500个恒星组成的目录,结果表明,没有假恒星,该新方法的效率是金字塔方法的4到5倍,几何方法的35到37倍。

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