Research on satellite laser ranging observation task scheduling method

Abstract

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.