TY - JOUR
T1 - A dynamic weapon target assignment based on receding horizon strategy by heuristic algorithm
AU - Zhang, Kai
AU - Zhou, Deyun
AU - Yang, Zhen
AU - Li, Xiaoyang
AU - Zhao, Yiyang
AU - Kong, Weiren
N1 - Publisher Copyright:
© 2020 Published under licence by IOP Publishing Ltd.
PY - 2020/11/25
Y1 - 2020/11/25
N2 - Weapon-target assignment problem is the crucial decision support in Command and Control system. As a typical operational scenario, asset defense has less research on the issue of asset-based dynamic weapon target assignment, and the major models of A-DWTA are challenging in practical application. In this paper, an A-DWTA model is first established by receding horizon decomposition strategy, which closely reflects the operational requirement. Then a heuristic algorithm based on statistical marginal return is proposed to solve the A-DWTA model. Experimental results show that the proposed algorithm for solving the A-DWTA model has advantages of real-time and robustness. The obtained decision plan can complete the operational mission in fewer stages and be adjusted adaptively by model and algorithm parameters.
AB - Weapon-target assignment problem is the crucial decision support in Command and Control system. As a typical operational scenario, asset defense has less research on the issue of asset-based dynamic weapon target assignment, and the major models of A-DWTA are challenging in practical application. In this paper, an A-DWTA model is first established by receding horizon decomposition strategy, which closely reflects the operational requirement. Then a heuristic algorithm based on statistical marginal return is proposed to solve the A-DWTA model. Experimental results show that the proposed algorithm for solving the A-DWTA model has advantages of real-time and robustness. The obtained decision plan can complete the operational mission in fewer stages and be adjusted adaptively by model and algorithm parameters.
UR - http://www.scopus.com/inward/record.url?scp=85097621827&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1651/1/012062
DO - 10.1088/1742-6596/1651/1/012062
M3 - 会议文章
AN - SCOPUS:85097621827
SN - 1742-6588
VL - 1651
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012062
T2 - 2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020
Y2 - 21 August 2020 through 23 August 2020
ER -