Yu Wenbo, Qu Hui, Zhang Yong
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Peng Cheng Laboratory, Department of Mathematics and Theory, Shenzhen 518055, China.
Sensors (Basel). 2023 Sep 12;23(18):7823. doi: 10.3390/s23187823.
A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors' attitude update rate by times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids' kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.
早期研究中提出的一种多曝光成像方法被用于将星敏感器的姿态更新率提高 倍。不幸的是，由于多次曝光，星图中也引入了严重的噪声。因此，本文提出了一种基于卡尔曼滤波器的星点质心提取方法。首先，基于质心的运动学模型生成星点预测窗口。其次，使用经典质心方法计算预测窗口内星点的粗质心。最后，分别对粗质心进行卡尔曼滤波器处理以滤除图像噪声，从而获得精细质心。进行了仿真以验证基于卡尔曼滤波器的估计模型。在零均值且最大偏差分别为±0.4、±1.0 和±2.5 像素的噪声下，滤波后的坐标误差分别降至原始误差的约 37.5%、26.3%和 20.7%。此外，进行了实验以验证星点预测窗口。在 100 幅星图中，计算出星点预测窗口获得的有效星点目标数量在每幅星图总目标数量中的平均比例仅为 0.95%。仿真和实验结果均证明了所提方法的可行性和有效性。
