Based on the radar equation and system noise characteristics, the maximum detection range model of a space debris laser ranging system at a 1064 nm wavelength is established, taking into account the factors of atmospheric transmission and sky background radiance. Through theoretical analysis and simulation experiments, the influencing factors of atmospheric transmission and sky background radiance are studied, and the influencing factors are normalized into the maximum detection range model by polynomial fitting. The results indicate that a high atmospheric transmission comes from a high altitude and low target zenith angle; a low sky background radiance comes from a small target zenith angle and low solar altitude angle, while the angular distance has no obvious influence on the sky background radiance. The experimental results indicate that the comprehensive accuracy of the maximum detection range model of the system is 86%, and the effectiveness of the model is verified by using a 1064 nm wavelength laser ranging for the debris target with a distance of 700–1100 km and a cross section area of 4–10 m². The model can be used to evaluate the ability of the space debris laser ranging system at a 1064 nm wavelength.

Satellite laser ranging (SLR) is a technology with the highest precision of single measurement of satellite radial distance, which is developing rapidly in the direction of long-distance, high-precision and automation. SLR autonomous observation task scheduling is an important step in realizing station automation, which needs to satisfy the principles of satellite tracking priority and maximization of observation revenue at the same time. In order to improve the automation and intelligence level of SLR system, based on the framework of ant colony optimization (ACO) algorithm, this paper combines the dynamic optimization characteristics of ACO algorithm and the local optimization characteristics of greedy algorithm, introduces the maximum-minimum ant mechanism, and puts forward a scheduling scheme for SLR observation task based on greedy ant colony algorithm (GACA). The results show that compared with the current scheduling method applied in practice, the number of observation satellites obtained from the GACA algorithm-based observation task planning for the SLR system has been improved by 37.4%, the total arc segment of satellite observation with higher priority has been extended by 36.47%, and the total observation gain has been increased by 42.39% in the same period of time. It effectively solves the problems of low efficiency, easy to miss satellites (being equipped with corner-cubes) and less satellites (being equipped with corner-cubes) stars in the observation process in manual scheduling, and provides a simple, practical, efficient and convenient observation task planning scheme for the establishment of an unmanned SLR system.

Observation task planning is a key issue and the first step in the development of automated Satellite Laser Ranging (SLR) systems. Aiming at the problem of dynamic change of cloud cover during SLR operation, this paper proposes an autonomous mission planning algorithm for SLR based on the Rolling Horizon Optimization (RHO) framework. A hybrid event- and cycle-driven replanning mechanism is adopted, and four functional modules, rolling, planning, information acquisition and decision-making, are established to decompose the SLR observation task planning process into a series of static planning intervals. An improved ant colony algorithm is proposed and utilized to realize the autonomous planning of SLR system observation tasks, and the above autonomous planning algorithm is verified and analyzed based on the SLR system at station 7237. The results show that the above algorithm can effectively increase the number of observation satellites and revenue under cloud disturbance, solve the problems of low efficiency and poor interference resistance of conventional static algorithms, and provide a new research idea for the establishment of an unattended SLR system.

Currently, multi-target survey telescope arrays play an important role in the build-up and maintenance of space object catalog databases, collecting massive observational data without attributing information. However, the matching process of massive observational data poses significant challenges to traditional prediction methods. To address the issues of low matching success rates and prolonged computation times in traditional methods, this paper proposes a multi-threaded data starting point matching method. First, orbital elements from the Space Surveillance and Tracking (SST) database are extracted for two days before and after the observation moment. A set of orbital elements closest to the observation epoch is filtered to form the primary candidate catalog containing the maximum number of objects. A matching error threshold is set. Second, multi-threaded traversal of the primary candidate catalog is performed to calculate observation residuals with the data starting point using an orbit prediction procedure. Orbital elements meeting the triple matching error threshold are selected to form the secondary candidate catalog, which is used in the entire data arc segment-matching calculation. Finally, the root mean square error (RMSE) of observation residuals for the entire data arc segment is computed point by point. The orbital elements satisfying the matching threshold are identified as matching results based on the principle of optimality. Experimental results demonstrate that with a matching error threshold of 1°, the proposed method achieves an average matching success rate of 97.62% for data arc segments with nearly 10,000 passes per day over 8 consecutive days. In the SST database containing an average of 25,720 targets, this method processes an average of 2164 data arc segments per minute, improving matching efficiency by 115 times compared to traditional prediction methods.

Currently, the management and maintenance of space target catalogues primarily rely on multi-target survey telescope arrays. However, the matching processing of massive observational data poses significant challenges to traditional prediction methods. To address the issues of low matching success rates and prolonged computation times in traditional methods, this paper proposes a data starting point matching method based on multithreading technology. First, orbital elements from the catalogue library are extracted for two days before and after the observation moment. A set of orbital elements closest to the observation moment is filtered to form the first-level matching catalogue library, and a matching error threshold is set. Second, multithreaded traversal of the first-level catalogue library is performed to calculate observation residuals using an orbit prediction procedure. Orbital elements meeting the triple matching error threshold are selected to form the second-level catalogue library. Finally, the root mean square error (RMSE) of observation residuals for the entire data arc segment is computed point by point. The identification of orbital elements satisfying the matching threshold is identified as matching results based on the principle of optimality. Experimental results demonstrate that with a matching error threshold of 1°, the proposed method achieves an average matching success rate of 97.62% for data arc segments with nearly 10,000 passes per day over 8 consecutive days. In a target catalogue library containing an average of 25,720 targets, the method processes an average of 2,164 data arc segments per minute, improving matching efficiency by 115 times compared to traditional prediction methods.