TY - GEN
T1 - Offset Parameter Optimization Method of Large-Scale Satellite Constellations Cooperative Observations on Area Targets
AU - Qi, Xinhu
AU - Liu, Xiaodong
AU - Hu, Zhijie
AU - Chen, Yanbin
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - It is necessary to segmenting area targets for large-scale satellite constellations cooperative observations on area targets while the differences of offset parameters affect observing efficiency. Optimizing the offset parameters can improve the efficiency of cooperative observation. However, it is difficult to explicitly express the relationship between the offset parameters and coverage. It is very time-consuming to optimize the offset parameters by iterative preprocessing and scheduling. The use of surrogate models can reduce computational costs, and improve the performance of optimization. Kriging method has good statistical properties of the overall situation, as the surrogate model for the global approximation. RBF network has the adaptive structure and characteristics of output being independent of the initial weights, as the surrogate model for local search. Simulation results show that surrogate model has good optimization results when used on optimizing the offset parameters of large-scale satellite constellations cooperative observations for area targets.
AB - It is necessary to segmenting area targets for large-scale satellite constellations cooperative observations on area targets while the differences of offset parameters affect observing efficiency. Optimizing the offset parameters can improve the efficiency of cooperative observation. However, it is difficult to explicitly express the relationship between the offset parameters and coverage. It is very time-consuming to optimize the offset parameters by iterative preprocessing and scheduling. The use of surrogate models can reduce computational costs, and improve the performance of optimization. Kriging method has good statistical properties of the overall situation, as the surrogate model for the global approximation. RBF network has the adaptive structure and characteristics of output being independent of the initial weights, as the surrogate model for local search. Simulation results show that surrogate model has good optimization results when used on optimizing the offset parameters of large-scale satellite constellations cooperative observations for area targets.
KW - Area target
KW - Earth observation satellite
KW - Parameter optimization
KW - Surrogate model
UR - http://www.scopus.com/inward/record.url?scp=85207825532&partnerID=8YFLogxK
U2 - 10.1109/ECNCT63103.2024.10704357
DO - 10.1109/ECNCT63103.2024.10704357
M3 - 会议稿件
AN - SCOPUS:85207825532
T3 - 2024 6th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024
SP - 12
EP - 19
BT - 2024 6th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024
Y2 - 19 July 2024 through 21 July 2024
ER -