Satellite mission planning for moving targets observation via data driven approach

Abstract

Satellite mission planning is an important premise for earth observation. Traditional satellite mission planning is mainly aimed at fixed ground targets, which cannot meet the increasingly complex mission requirements. This paper considers the moving target observation, and puts forward a method of satellite mission planning via data driven approach. This method forecasts the future track and position information of the moving target through a modified Long Short-Term Memory (M-LSTM) networks algorithm, and proposes a Constraint Satisfaction Genetic Algorithm (CSGA) to plan the missions of moving target observation based on the results of M-LSTM algorithm. In view of the complexity of the constraint and task conflict in the moving target observation, CSGA embeds the constraints into the genetic algorithm through conditional forms, and a conflict resolution operator is designed in CSGA to resolve task conflicts. Simulation results demonstrate the efficiency of the LSTM-CSGA method to solve the mission planning and get a higher observation accuracy.