Weapon-Target Assignment Optimization Based on Multi-attribute Decision-making and Deep Q-Network for Missile Defense System

XIE Junwei; FANG Feng; PENG Dongliang; REN Jinlei; WANG Changping

doi:10.11999/JEIT211136

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3833-3841

XIE Junwei, FANG Feng, PENG Dongliang, REN Jinlei, WANG Changping. Weapon-Target Assignment Optimization Based on Multi-attribute Decision-making and Deep Q-Network for Missile Defense System[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3833-3841. doi: 10.11999/JEIT211136

Citation:

XIE Junwei, FANG Feng, PENG Dongliang, REN Jinlei, WANG Changping. Weapon-Target Assignment Optimization Based on Multi-attribute Decision-making and Deep Q-Network for Missile Defense System[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3833-3841. doi: 10.11999/JEIT211136

Citation:

XIE Junwei, FANG Feng, PENG Dongliang, REN Jinlei, WANG Changping. Weapon-Target Assignment Optimization Based on Multi-attribute Decision-making and Deep Q-Network for Missile Defense System[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3833-3841. doi: 10.11999/JEIT211136

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Weapon-Target Assignment Optimization Based on Multi-attribute Decision-making and Deep Q-Network for Missile Defense System

doi: 10.11999/JEIT211136

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
China Academy of Launch Vehicle Technology, Beijing 100076, China

Funds: The National Natural Science Foundation of China(61673146), Zhejiang Provincial University Research Foundation (GK209907299001-021)

Received Date: 2021-10-15
Accepted Date: 2022-01-14
Rev Recd Date: 2022-01-10

Available Online: 2022-02-02

Publish Date: 2022-11-14

Abstract

Abstract

In a large-scale Weapon-Target Assignment (WTA) problem, the explored solution space becomes enormous due to the curse of dimensionality, and it causes low-efficiency in searching optimization solution. For solving this problem effectively, a WTA optimization approach based on multi-attribute decision-making and Deep Q-Network (DQN) is proposed. Firstly, a threat-assessment model for attacking missiles is built based on the approach of Analytic Hierarchy Process (AHP). Meanwhile, an entropy method, used for evaluating the differences of target attributes, is introduced, to increase objective in computing threat-assessment results. Then, an assignment criterion of maximum intercept probability is designed based on assess results, and a multi-steps WTA decision model is built in DQN frame. A uniform experience sampling strategy is designed, making sure that each target type of assignment experience has the same probability to be selected. Furthermore, for balancing the DQN convergence speed and global optimum, a reward function that combines local and global rewards is designed. Lastly, simulation results shows that the proposed WTA approach has the advantage in solving large-scale WTA problem fast and effectively, compared with the general heuristic approach. Also, it presents the robust performance for WTA scenario elements variation.
- Weapon Target Assignment(WTA),
- Deep Q Network(DQN),
- Threat assessment,
- Improved Analytic Hierarchy Process(AHP)

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References(16)

References

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