Many studies have been conducted about angular velocity estimation using star sensors, and fairly good performance is achieved when the spacecraft is working at low-dynamic environments. However, when the spacecraft rotates at a large angular velocity, the star image will become blurred, which makes it difficult to identify and recognize star centroids, which, in turn, reduces the accuracy of angular velocity estimation. Therefore, to solve the problem with angular velocity estimation in highly dynamic situations, this research studies a method of angular velocity estimation using blurred star images. The length and ending point coordinates of star trails obtained from these blurred star images are used for a series of processes, which includes the pre-processing of blurred star images, determination of starting and ending points, thinning and selection of star trails, and calculation of trail lengths. Simulations show that the effectiveness of the proposed method in highly dynamic situations and the angular velocity estimation errors in constant and sinusoidal variation are reduced by at least 66% and 62%, respectively, compared with those of the traditional method.

已经使用星形传感器进行了许多有关角速度估计的研究，当航天器在低动态环境下工作时，可以获得相当好的性能。但是，当航天器以较大的角速度旋转时，恒星图像将变得模糊，这使得难以识别和识别恒星质心，从而降低了角速度估计的准确性。因此，为解决高动态情况下的角速度估计问题，本研究研究了一种利用模糊星图估计角速度的方法。从这些模糊星图获得的星迹的长度和终点坐标用于一系列过程，包括对模糊星图的预处理，起点和终点的确定，星迹的细化和选择，以及迹线长度的计算。仿真表明，与传统方法相比，该方法在高动态情况下的有效性以及恒定和正弦变化中的角速度估计误差分别降低了至少66％和62％。