Dynamic Decision-making Method of Unmanned Platform Chaff Jamming for Terminal Defense Based on Multi-agent Deep Reinforcement Learning

doi:10.12382/bgxb.2024.0251

Abstract

Abstract:

Chaff centroid jamming of unmanned platform is an important means of missile terminal defense.The intelligent decision-making ability in platform maneuvering and chaff launching is an important factor to determine whether the strategic assets can be protected successfully.The current decision-making methods,such as computational analysis based on mechanism model and space exploration based on heuristic algorithm,have the problems of low degree of intelligence,poor adaptability and slow decision-making speed.A dynamic decision-making method of chaff jamming for terminal defense based on multi-agent deep reinforcement learning is proposed.The problem of cooperative chaff jamming of multi-platform for terminal defense is defined,and a simulation environment is constructed.The missile guidance and fuze model,unmanned jamming platform maneuvering model,chaff diffusion model and centroid jamming model are established.The centroid jamming decision problem is transformed into a Markov decision problem,a decision-making agent is constructed,the state and action spaces are defined,and a reward function is set.The decision-making agent is trained by using the multi-agent proximal policy optimization (MAPPO) algorithm.The simulated results show that the proposed method reduces the training time by 85.5% and increases the success rate of asset protection by 3.84 compared with the multi-agent deep deterministic policy gradient (MADDPG) algorithm.Compared with the GA,it reduces the deciding time by 99.96 % and increases the success rate of asset protection 1.12.

Key words: unmanned platform, centroid jamming, chaff jamming, terminal defense, multi-agent reinforcement learning, electronic countermeasure

LI Chuanhao, MING Zhenjun, WANG Guoxin, YAN Yan, DING Wei, WAN Silai, DING Tao. Dynamic Decision-making Method of Unmanned Platform Chaff Jamming for Terminal Defense Based on Multi-agent Deep Reinforcement Learning[J]. Acta Armamentarii, 2025, 46(3): 240251-.

Figures/Tables 18

Fig.1 Schematic diagram of multi-platform chaff terminal defense

Fig.2 Composition of multi-platform chaff terminal defense model

Fig.3 Relative motion relationship of missile and target

Fig.4 Operation flow of chaff jamming terminal defense agent

Table 1 Representation of jamming decision state space

状态类型	状态名称	状态标识	维度	取值范围
威胁信息	导弹位置坐标/m	$o i, t m p$	N_mis×2	[X_min,X_max]×[Y_min,Y_max]
	导弹方向坐标/rad	$o i, t m a$	N_mis	[-πrad,πrad)
	导弹机动速率/(m·s^-1)	$o i, t m v$	N_mis	[0, $v m a x m i s$ ]
	导弹跟踪时长/s	$o i, t m t$	N_mis	[0,T_max)
弹药信息	箔条状态矩阵/m	$o t c s$	$N m a x c h a f f$ ×3	[X_min,X_max]×[Y_min,Y_max]×[0,k]
弹药信息	箔条弹剩余数量	$o t c l$	N_plat	{0,1…, $N c h a f f m a x$ }
无人干扰平台信息	位置坐标/m	$o t c p$	2	[X_min,X_max]×[Y_min,Y_max]
	方向坐标/rad	$o t c a$	1	[-πrad,πrad)
	速率值/(m·s^-1)	$o t c v$	1	[0, $v m a x c a r$ ]
任务信息	资产位置/m	$o t a p$	N_mis×2	[X_min,X_max]×[Y_min,Y_max]
任务信息	资产威胁距离/m	$o t a l$	N_mis	[0, $(X m a x - X m i n) 2 + (Y m a x - Y m i n) 2$ ]

Table 1 Representation of jamming decision state space

状态类型	状态名称	状态标识	维度	取值范围
威胁信息	导弹位置坐标/m	$o i, t m p$	N_mis×2	[X_min,X_max]×[Y_min,Y_max]
	导弹方向坐标/rad	$o i, t m a$	N_mis	[-πrad,πrad)
	导弹机动速率/(m·s^-1)	$o i, t m v$	N_mis	[0, $v m a x m i s$ ]
	导弹跟踪时长/s	$o i, t m t$	N_mis	[0,T_max)
弹药信息	箔条状态矩阵/m	$o t c s$	$N m a x c h a f f$ ×3	[X_min,X_max]×[Y_min,Y_max]×[0,k]
弹药信息	箔条弹剩余数量	$o t c l$	N_plat	{0,1…, $N c h a f f m a x$ }
无人干扰平台信息	位置坐标/m	$o t c p$	2	[X_min,X_max]×[Y_min,Y_max]
	方向坐标/rad	$o t c a$	1	[-πrad,πrad)
	速率值/(m·s^-1)	$o t c v$	1	[0, $v m a x c a r$ ]
任务信息	资产位置/m	$o t a p$	N_mis×2	[X_min,X_max]×[Y_min,Y_max]
任务信息	资产威胁距离/m	$o t a l$	N_mis	[0, $(X m a x - X m i n) 2 + (Y m a x - Y m i n) 2$ ]

Table 2 Representation of jamming decision action space

动作类型	动作名称	动作标识	取值范围
弹药动作	是否发射箔条	$a i, t i c$	{0,1}
	箔条发射方向/rad	$a i, t c a$	[-πrad,πrad)
	箔条发射距离/m	$a i, t c d$	[0, $L m a x c h a f f$ ]
无人干扰平台信息	平台移动方向/rad	$a i, t p d$	[-πrad,πrad)
无人干扰平台信息	平台移动速度/(m·s^-1)	$a i, t p s$	[0, $v m a x c a r$ ]

Table 2 Representation of jamming decision action space

动作类型	动作名称	动作标识	取值范围
弹药动作	是否发射箔条	$a i, t i c$	{0,1}
	箔条发射方向/rad	$a i, t c a$	[-πrad,πrad)
	箔条发射距离/m	$a i, t c d$	[0, $L m a x c h a f f$ ]
无人干扰平台信息	平台移动方向/rad	$a i, t p d$	[-πrad,πrad)
无人干扰平台信息	平台移动速度/(m·s^-1)	$a i, t p s$	[0, $v m a x c a r$ ]

Fig.5 MAPPO algorithm training framework

Table 3 Performance parameters of red and blue forces equipment

阵营	性能参数	标识	取值
蓝方	导弹近炸距离/m	L_fuse	60
	导弹比例导引系数	K	5
	导弹机动速率/(m·s^-1)	v^mis	150
	导弹最大角速度/(rad·s^-1)	$ω m a x m i s$	π/6
	资产雷达反射截面积/m²	σ^a	5000
红方	资产引爆点安全距离/m	$L a s s e t_s f s a f e$	60
	无人干扰平台最大速率/(m·s^-1)	$v m a x p l a t$	20
	无人干扰平台最大角速度/(rad·s^-1)	$ω m a x p l a t$	π/3
	无人干扰平台最大加速度/(m·s^-2)	$a m a x p l a t$	5
	箔条云持续时间/s	$T a l i v e c h a f f$	2
	箔条最大发射距离/m	$L m a x c h a f f$	10
	箔条武器装填量	$N m a x c h a f f$	5
	箔条最大反射截面积/m²	k	1000

Table 3 Performance parameters of red and blue forces equipment

阵营	性能参数	标识	取值
蓝方	导弹近炸距离/m	L_fuse	60
	导弹比例导引系数	K	5
	导弹机动速率/(m·s^-1)	v^mis	150
	导弹最大角速度/(rad·s^-1)	$ω m a x m i s$	π/6
	资产雷达反射截面积/m²	σ^a	5000
红方	资产引爆点安全距离/m	$L a s s e t_s f s a f e$	60
	无人干扰平台最大速率/(m·s^-1)	$v m a x p l a t$	20
	无人干扰平台最大角速度/(rad·s^-1)	$ω m a x p l a t$	π/3
	无人干扰平台最大加速度/(m·s^-2)	$a m a x p l a t$	5
	箔条云持续时间/s	$T a l i v e c h a f f$	2
	箔条最大发射距离/m	$L m a x c h a f f$	10
	箔条武器装填量	$N m a x c h a f f$	5
	箔条最大反射截面积/m²	k	1000

Table 4 Value of parameters in MAPPO algorithm

参数	标识	数值
策略网络学习率	α	5×10^-4
价值网络学习率	β	5×10^-4
最大迭代步数	step_max	3×10⁵
数据缓冲区容量	B	5
评估测试间隔	T_ite	50
每轮最大步数	T_max	30
连续更新次数	K	5
截断参数	ε	0.2
策略熵系数	φ	0.01
折扣因子	γ	0.999
GAE参数	λ	0.95
策略/价值网络层数	N_layer	4
隐藏层神经元个数	N_hidden	64
测试环境个数	N_env	100

Table 5 Test environment parameters

环境序号	资产位置/m	车辆方向/ rad	导弹位置/m
1	$3345.9 4790.8 4354.1 3048.8 4933.4 4892.2$	$2.59 - 0.32 2.88$	$482.6 3185.1 2917.6 5889.4 2616.3 2769.1$
2	$3204.0 4596.9 3386.2 3040.0 4990.6 3491.9$	$1.22 0.27 2.84$	$5536.7 6601.3 5096.9 5639.4 7012.2 5877.6$
︙	︙	︙	︙
100	$3193.3 4789.2 4423.6 3249.6 4903.9 4866.0$	$0.01 - 0.97 - 2.95$	$149.4 5490.2 1595.4 1478.2 3777.5 2043.0$

Table 5 Test environment parameters

环境序号	资产位置/m	车辆方向/ rad	导弹位置/m
1	$3345.9 4790.8 4354.1 3048.8 4933.4 4892.2$	$2.59 - 0.32 2.88$	$482.6 3185.1 2917.6 5889.4 2616.3 2769.1$
2	$3204.0 4596.9 3386.2 3040.0 4990.6 3491.9$	$1.22 0.27 2.84$	$5536.7 6601.3 5096.9 5639.4 7012.2 5877.6$
︙	︙	︙	︙
100	$3193.3 4789.2 4423.6 3249.6 4903.9 4866.0$	$0.01 - 0.97 - 2.95$	$149.4 5490.2 1595.4 1478.2 3777.5 2043.0$

Fig.6 Average cumulative reward value curve for each training cycle

Fig.7 Average protection success rate curve of each training cycle

Fig.8 The multi-agent terminal defense simulation process after training

Fig.9 The change in dfistance of asset from threat in a single simulation

Table 6 Comparison results of terminal defense performances

算法	训练时长/s	平均每轮决策时长/s	平均成功率			平均最小资产威胁距离/m
算法	训练时长/s	平均每轮决策时长/s	资产1	资产2	资产3	资产1	资产2	资产3
MAPPO算法	2763.07	0.063277	0.75	0.85	0.77	85.27	108.75	91.63
MADDPG算法(1000)	19102.6	0.022671	0.18	0.22	0.01	34.14	33.18	13.20
MADDPG算法(10000)	35916.8	0.020543	0.09	0.20	0.15	26.36	32.23	26.33
MADDPG算法(100000)	28220.1	0.017842	0.07	0.16	0.26	29.35	37.57	39.71
GA		148.68	0.49	0.49	0.14	71.98	67.45	27.08

Fig.10 Changes in the success rates of asset protection of agent trained by MAPPO and MADDPG algorithm

Fig.11 Variation of effect and decision time of GA with optimization generation

Fig.12 The influence of agent reward setting on the convergence process of the algorithm

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