Novel Inertial Instrument and Navigation System Technology Laboratory, School of Instrumentation Science and Optoelectronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China.
Sensors (Basel). 2010;10(3):1955-66. doi: 10.3390/s100301955. Epub 2010 Mar 10.
A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.
一种基于自适应蚁群（AAC）算法的新的恒星识别方法已经被开发出来，以提高星敏感器的恒星识别速度和成功率。该方法绘制圆圈，每个圆圈的中心是一个亮星点，半径是一个特殊的角度距离，并利用 AAC 算法的并行处理能力来计算圆圈中任意两个星点之间的角度距离。圆圈中两个星点的角度距离被求解为 AAC 算法的路径，并且利用 AAC 的路径优化特性来搜索圆圈中的最佳（最短）路径。这条最佳路径用于识别恒星图，从而提高识别成功率和速度。实验结果表明，当位置误差约为 50″时，该方法的识别成功率为 98%，而 Delaunay 识别方法仅为 94%。该方法的识别时间高达 50ms。
