Remote sensing satellite ground station antenna intelligent scheduling with LSTM and heuristic search

doi:10.7523/j.ucas.2020.0014

Abstract

Abstract: In order to solve the shortage of remote sensing satellite data receiving antennas and improve the utilization of the ground antennas, an intelligent scheduling method which combines LSTM (long short-term memory network) and heuristic search was proposed. First, LSTM is used to extract the antenna using rules from the historical scheduling data of antennas, and then the initial scheduling scheme is obtained by allocating an antenna for each remote sensing data receiving task with the rules; Second, the heuristic search is used to solve the two problems of joint data reception and resource selection conflict in the initial plan, and obtain a practical and feasible scheduling plan. The experiment results show that the method is useful to deal with ground antenna scheduling, improve resource efficiency and reduce computing time to some extent when compared with genetic algorithm.

Key words: remote sensing satellite, ground station antennas scheduling, long short-term memory (LSTM) neural network, heuristic search, intelligent scheduling

CLC Number:

O221.7
V556

SUN Wenjun, MA Guangbin, TIAN Miaomiao, LIN Youming, HUANG Peng. Remote sensing satellite ground station antenna intelligent scheduling with LSTM and heuristic search[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(4): 532-542.

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