Optimization Design of Regional Coverage Remote Sensing Constellation Based on Improved Particle Swarm Optimization Algorithm

To achieve regional coverage of low orbit remote sensing constellation optimization design, this paper proposes an improved particle swarm optimization algorithm with compression factor, which integrates the golden sine algorithm. Compared with traditional particle swarm optimization algorithms, it can better control particle velocity and avoid getting stuck in local optimal solutions during the optimization process. This article is based on the 6N constellation configuration, with the average revisit time and average coverage time as optimization objectives, and applies an improved particle swarm optimization algorithm to optimize the parameters of satellite orbit. Simulation results show that the improved particle swarm optimization algorithm can converge the minimum average revisit time to 6.5 minutes and the maximum average coverage time to 30 minutes, which is better than the optimization results of the traditional particle swarm algorithm, and can effectively achieve the regional coverage task in Changchun area.