基于RF-XGBoost算法的无人机多回合攻防博弈决策

doi:10.12305/j.issn.1001-506X.2025.02.18

Abstract

Abstract:

To solve the multi-round game confrontation problem of unmanned aerial vehicles (UAVs) with unbalanced air combat data set, a random forest-eXtreme gradient boosting (RF-XGBoost) algorithm is proposed to study the attack and defense game decision-making. The payment matrix model is established by analyzing the movement status and air combat information of the red and blue sides, then, the linear induction method is considered to solve the current Nash equilibrium solution and expected return of the game, and whether the game confrontation will stop depends on the victory of the blue side. In the process of game confrontation, in the first place, the feature dimensionality reduction of air combat data set is conducted based on the random forest (RF) algorithm to improve the real-time performance of air combat decision-making. Then, an improved XGBoost algorithm is proposed to deal with the unbalanced data set, which is used to determine the optimal maneuvers to improve the accuracy of maneuver decision-making and enhance blue confrontation's situation, and air combat information of the next round of red and blue sides is obtained. Furthermore, the Nash equilibrium solution and expected return based on the payment matrix model of the next round can be obtained once again, until the blue side wins. Finally, the feasibility and effectiveness of the proposed algorithm are verified by simulation test.

Key words: unmanned aerial vehicle (UAV), random forest (RF), eXtreme gradient boosting (XGBoost), multi-round game

CLC Number:

TP391.9

Shipei ZOU, Yuhui WANG, Hongrui LIU. Multi-round attack and defense game decision-making of UAVs based on RF-XGBoost algorithm[J]. Systems Engineering and Electronics, 2025, 47(2): 518-526.

Figures/Tables 14

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输入特征	编号
(x_A, y_A, z_A)	e₁
V_A	e₂
θ_A	e₃
Ψ_A	e₄
(x_B, y_B, z_B)	e₅
V_B	e₆
θ_B	e₇
Ψ_B	e₈

输出属性	编号
匀速属性	w₁
左转弯	w₂
右转弯	w₃
加速	w₄
减速	w₅
俯冲	w₆
爬升	w₇

编号	x_A	y_A	z_A	V_A	θ_A	Ψ_A	x_B	y_B	z_B	V_B	θ_B	Ψ_B
4	53	51	2.3	197	30	4.1	25	7	6.15	150	10	13.3
4	35	11	7.73	170	4	15.2	3	35	8.58	158	0	16
3	17	7	5.12	198	3	9.6	50	51	6.99	178	20	1.3
6	42	12	11.55	178	16	13.6	43	10	4.98	178	19	18.5
2	2	59	6.75	155	27	12.5	55	23	8.96	177	12	11.4

编号	y_A	z_A	θ_A	x_B	y_B	z_B	V_B	Ψ_B
4	36	4.58	25	60	7	7.63	157	11.1
4	8	8.70	10	29	11	1.91	145	4.9
3	15	7.02	5	33	48	8.83	179	4.8
6	41	8.66	7	48	40	5.68	178	5.1
2	29	5.23	1	3	13	5.90	176	2.6

算法处理	模型平均训练时间/s
初始数据集训练模型	4.493
RF特征降维后训练模型	3.515

Multi-round attack and defense game decision-making of UAVs based on RF-XGBoost algorithm

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Figures/Tables 14

References 30

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博弈对抗算法处理	博弈平均仿真时间/s
未降维处理的改进XGBoost算法	33.122
RF特征降维后的改进XGBoost算法	24.014
RF特征降维后的传统决策树算法	29.483

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