Optimization Method for LEO Constellation Deployment Strategy Using Improved Genetic Algorithm
Considering the time constraints of mission interval at the launch site,an optimization method for LEO(low earth orbit)constellation deployment strategy is proposed in this paper.A dynamic modeling of the deployment process for low earth circular orbit constellation is conduc-ted,and the relationship between the deployment window and the phase difference of the orbit in-sertion point are derived,as well as the cost analysis of phase adjustment after orbit insertion.The combination of constellation deployment position sequence is taken as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,which simplifies a high-dimensional search problem within a wide range of dates to a finite-dimensional integer pro-gramming problem.An improved genetic algorithm for local search is introduced to optimize the launch deployment strategy computation.As verification,an example of a 9-satellites constellation de-ployment is optimized.The result shows that the optimization method reduces the computational com-plexity by 7 orders of magnitude compared with the exhaustion search method and can quickly obtain the optimal result that satisfies the constraint of the launch interval and takes into account the deployment time and the cost of orbital transfer using the evolutionary calculations within 50 generations.
LEO constellation deploymentstrategy optimizationlaunching schedule constraintimproved genetic algorithm
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