Star spot extraction based on optimal background estimation for star sensor anti stray light

In this article, the problem of extracting star spots for star sensors in stray light is considered. Under this setting, the effect of extracting star spots becomes worse, which affects the accuracy and reliability of the estimated attitude. An extraction method based on optimal surface fitting is proposed for this task. First, the imaging characteristics of stray light in star sensor are analyzed, and the surface model with a closed-form solution is built. Then, the method of estimating optimal stray light background and extracting star spots is proposed. The performance of the proposed method is verified by the simulated star images and star sensor images. Experimental results show that the detection rate, false detection rate and centroid accuracy obtained by the proposed method are better than that obtained by the methods based on threshold and morphological, which shows that the proposed method can resist the disturbance of stray light.

In order to address the issues of low number of star points extracted under stray light interference and poor centroid accuracy in star sensors, this paper proposes an anti stray light star point extraction method based on optimal background estimation of stray light. This method analyzes and constructs a surface model of stray light, and designs a method for estimating the optimal background of stray light and a star extraction method. After simulation experiments and physical experiments, the performance of this method is superior to existing background based extraction methods and morphology based methods in terms of detection rate, false detection rate, and centroid accuracy. This indicates that the method has good anti stray light ability. In practice, the imaging characteristics of stray light are diverse, and more testing environments will be constructed to further test and analyze the algorithm in the future.