为了获得高精度、高更新率的抗噪声性能，对星敏感器星像提取环节进行了研究。首先，分析星图中星像灰度的分布特点，建立了判断某个像素是否与峰值像素归属同一星像的标准。然后，介绍了像元阵列分块方法和背景预测法。最后，结合星像的特点提出了以峰值点为种子点的区域生长准则。仿真实验结果表明，在不加噪声的情况下，提取出的星像与参考星图完全一致，用质心法得到的亚像素定位精度为0.028 2。在添加均值为20、标准差高达2.5的强高斯灰度噪声的情况下，提取率仍能达到86.11%，质心精度则下降到0.219 6 pixel。均匀性很差，信噪比低于4.9 dB的实拍星图实验结果也证明该方法有很强的星像提取能力和准确性，能够满足强噪声弱星像质心提取的强抗干扰能力的要求。

To improve the precision, update rate and the anti-noise ability of star sensors, this study was focused on the extraction of star images. Firstly, the criterion to confirm whether a pixel attributes to the same star coverage with the local maximum pixel was established according to the gray distributing features of star images. Then, several kinds of methods for image extraction were introduced, such as the block division of imaging array, and the prediction of background noise. Finally, by regarding the peak value pixel as the origin of the circular region growing, the local region growing criterion was set up successfully based on the gray distributing features of star images. The simulation conducted in the case of no noises shows that all of the simulated star images in the reference star map can be extracted successfully, and the sub-pixel location accuracy is 0.028 2 pixel by using centroid method. Moreover, under upmost adverse condition with a Gaussian noise mean value of 20 and standard variance as high as 2.5, the success extraction rate can still reach 86.11% with a decreased centroiding accuracy of 0.219 6 pixel. The test about a faint star image in a star map with poor uniformity and a low SNR of 4.9 dB also proves the excellent detection ability of the proposed method, and it shows advantages of good real-time property and high correct extracting rate for star images.