Wide-field telescopes with long exposure times have stronger space target detection capabilities. However, complex background sky conditions will still cause a series of difficulties in detecting space debris, such as a large number of star points, a large amount of noise, and the discontinuity and nonlinearity of the target. We propose a space debris automatic extraction channel with a high detection rate and low computational cost to solve these difficulties. We apply an improved median filter for noise elimination and then the double-structure morphological filter algorithm used to suppress the background of the star image to eliminate star points and noise. Then, the guided filter was used to eliminate residual noise, and star points were used to reduce the impact on the target. Finally, the improved Hough transform was also applied to detect the target in the image. Our automatic extraction algorithm is used in real astronomical star maps, including different orbiting satellites (star-tracking mode). These images were obtained by using a 280 mm diameter telescope, which was located in Changchun Observatory. The experimental results demonstrated the effectiveness of the extraction algorithm in this study. It can effectively detect and track space targets in a long-exposure wide-field surveillance system and has high positioning accuracy and low computational complexity, which solves the problem of space debris extraction under a complex background.

The resource constrained multi-project scheduling problem (RCMPSP) is an important issue in business applications. In this paper, a modified differential evolution algorithm is introduced to achieve higher computational efficiency for RCMPSP. Two parallel mutation operations are used to improve the search ability with a modified DE/rand-best/1/bin strategy. The selection operation is used to choose best individual from target vector and two trail vector. The computational results show that the modified DE algorithm performs better than several other algorithms of deterministic and heuristic nature.

In the past few years, the increasing amount of space debris has triggered the demand for distributed surveillance systems. Long exposure time can effectively improve the target detection capability of the wide-area surveillance system. Problems that also cause difficulties in space-target detection include large amounts of data, countless star points, and discontinuous or nonlinear targets. In response to these problems, this paper proposes a high-precision space-target detection and tracking pipeline that aims to automatically detect debris data in space. First, a guided filter is used to effectively remove the stars and noise, then Hough transform is used to detect space debris, and finally Kalman filter is applied to track the space debris target. All experimental images are from Jilin Observatory, and the telescope is in star-tracking mode. Our method is practical and effective. The results show that the proposed automatic extraction channel of space debris can accurately detect and track space targets in a complex background.