Robustness Study of Non-Dimensional Star Pattern Recognition for a Typical Star Tracker

Non-Dimensional star pattern recognition uses planar angles as its recognition feature. This feature is independent of image focal length and optical axis offset. However, this independency doesn’t mean that the algorithm conserves its robustness in presence of any type of errors. These errors arise from poor hardware calibration and software inaccuracy that causes the angles to differ from their true amounts stored in the database. In order to evaluate the effect of angle differences on algorithm performance, overall disposition of bright point centers is modeled. The monte-carlo simulation method is then used to evaluate the algorithm’s performance for different amounts of error. Results demonstrate that 0.1 pixel size error is admissible in conserve the trade-off between desired update frequency, hardware accuracy and algorithm’s robustness.