Spacecraft identification and tracking, which are core technologies in the aero-space field, are crucial for ensuring the safety and effectiveness of spacecraft operation in celestial bodies. However, in the space environment, dense stars interfere with the recognition of targets, and complex and variable lighting conditions may result in incomplete imaging, thereby affecting the clear presentation of targets. Therefore, a spacecraft recognition and positioning algorithm based on star-point recognition and multiframe aggregation is proposed in this study to accurately identify spacecraft targets within the proximity of a camera. This algorithm first maps star-point information to the spacecraft imaging plane via star-map recognition, thus eliminating dense interfering star points in the background of celestial bodies. Subsequently, a target-aggregation algorithm and a continuous-frame local-area localization method are introduced to address the incomplete spacecraft imaging and limited information of single-frame spacecraft due to lighting. Experimental results show that the proposed algorithm effectively eliminates more than 97% of interfering star points and satisfies the spacecraft positioning accuracy within a deviation range of $0.2^{\circ}$, thus demonstrating its high accuracy and robustness.
WU Qun
,
ZENG Dan
,
CHEN Hongyu
,
XIE Xianghua
,
CHANG Liang
. Spacecraft recognition and localization based on star-point identification and multiframe aggregation[J]. Journal of Shanghai University, 2026
, 32(2)
: 283
-294
.
DOI: 10.12066/j.issn.1007-2861.2549
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