Plume Noise Suppression Algorithm for Missile-Borne Star Sensor Based on Star Point Shape and Angular Distance between Stars

Abstract

When a missile is launched, the plume generated by the propulsion system will produce a lot of fake stars in the star image, which will affect the normal work of the missile-borne star sensor. A plume noise suppression algorithm based on star point shape and angular distance between stars is proposed in this paper, which is a preprocessing algorithm for star identification. Firstly, principal component analysis is used to extract the shape features of star points. Secondly, the authenticity of star points is evaluated based on length-width ratios. Thirdly, in two consecutive frames of star images, according to the shape features of star points, the optimal matching window is determined to achieve accurate matching of the corresponding star points. Finally, the rapid elimination of fake stars is completed by the principle of invariant angular distance between true stars. Simulation experiment results show that the proposed algorithm is quite robust and fast, and the elimination ratio is high even if the number of fake stars reaches four times more than true stars. Compared with the existing star identification algorithms, when the number of fake stars is large, the advantage of the proposed algorithm is obvious. Experimentation on actual star images verifies that the proposed algorithm can meet the requirements of spacecraft even if there are a large number of fake stars in the star image.

Keywords

• star sensor
• plume noise
• principal component analysis
• shape features
• angular distance