A Real-time Faint Space Debris Detector With Learning-based LCM
Zherui Lu Gangyi Wang Xinguo Wei Jian Li
With the development of aerospace technology, the increasing population of
space debris has posed a great threat to the safety of spacecraft. However, the
low intensity of reflected light and high angular velocity of space debris
impede the extraction. Besides, due to the limitations of the ground
observation methods, small space debris can hardly be detected, making it
necessary to enhance the spacecraft's capacity for space situational awareness
(SSA). Considering that traditional methods have some defects in low-SNR target
detection, such as low effectiveness and large time consumption, this paper
proposes a method for low-SNR streak extraction based on local contrast and
maximum likelihood estimation (MLE), which can detect space objects with SNR
2.0 efficiently. In the proposed algorithm, local contrast will be applied for
crude classifications, which will return connected components as preliminary
results, and then MLE will be performed to reconstruct the connected components
of targets via orientated growth, further improving the precision. The
algorithm has been verified with both simulated streaks and real star tracker
images, and the average centroid error of the proposed algorithm is close to
the state-of-the-art method like ODCC. At the same time, the algorithm in this
paper has significant advantages in efficiency compared with ODCC. In
conclusion, the algorithm in this paper is of high speed and precision, which
guarantees its promising applications in the extraction of high dynamic
targets.