Space debris detection and positioning method based on star map recognition

Space-based surveillance of space debris, as a crucial component of space situational awareness, holds significant importance for understanding space situations and protecting space resources. Space debris, far from the Earth, exhibits characteristics such as small size and weak energy. Compared to other large targets, space debris imaging resembles that of background stars, occupying a small number of pixels, thus posing great challenges for detection and positioning. Conventional space point target detection methods (such as image differencing, trajectory detection, and matched correlation) prove ineffective, thereby compromising the efficient and real-time surveillance performance of space-based surveillance platforms for dim and small targets. By applying the performance advantages of star trackers to space debris surveillance, this paper utilizes the star pattern identification principle of star trackers to distinguish space debris from stars, enabling rapid and effective detection of space debris. To address the positioning of dim and small targets against a star pattern background, a target search and positioning algorithm combining window scanning with the Kalman filter is proposed. Using the target's initial state established during star pattern identification, the target observation coordinates are determined through window scanning at predicted positions, and high-precision target positioning is achieved by the Kalman filter. Experimental tests on the algorithm's performance reveal that it can effectively accomplish target identification and positioning tasks under different noise intensities. When the standard deviation of Gaussian noise is ten, the algorithm can achieve a target positioning accuracy of 2.15″.