基于混合模型的异构无人机蜂群效能评估

doi:10.16182/j.issn1004731x.joss.22-1294

摘要/Abstract

摘要：

为实现无人机蜂群效能的快速评估，提出一种基于ADC（availability dependability capability）系统效能评估和BP神经网络预测的混合模型，以应对无人机蜂群配置和状态的多样性以及效能计算的复杂性。在分析蜂群效能构成要素的基础上，建立包含无人机通用平台能力，系统级能力，以及任务执行能力的能力指标体系。利用ADC法生成蜂群作战效能样本集合，运用BP神经网络构建关于无人机参数和能力指标的综合作战效能评估模型。利用该评估模型实现异构无人机蜂群实例的综合作战效能评估。结果表明：该模型评估误差可达5%以下，基于样本的评估时间可达3 h以内，验证了该模型在异构无人机蜂群效能评估中的有效性及高效性。同时，通过分析数量、配置对无人机蜂群综合效能的影响，获得了异构无人机蜂群配置的可行建议。

关键词: 异构无人机, 蜂群系统, 效能评估, ADC-BP神经网络, 混合模型

Abstract:

This paper presents a hybrid model based on availability dependability capability (ADC) system performance evaluation and back propagation (BP) neural network prediction to realize a rapid performance evaluation of UAV swarms and cope with the diversity of UAV swarm configuration and state and the complexity of performance calculation. By analyzing the components of swarm performance, a capability index system including the general platform capability, system-level capability, and task execution capability of UAVs is established.By using the ADC method, a swarm combat performance sample set is generated, and the BP neural network is used to construct a comprehensive combat performance evaluation model of UAV parameters and capability indexes. The evaluation model is used to evaluate the comprehensive combat performance of heterogeneous UAV swarms. The results show that the evaluation error of this model can reach less than 5%, and the evaluation time based on samples is less than three hours, which verifies the effectiveness and high efficiency of this model in the evaluation of heterogeneous UAV swarm performance. At the same time, by analyzing the influence of quantity and configuration on the comprehensive performance of UAV swarms, feasible suggestions on the configuration of heterogeneous UAV swarms are obtained.

Key words: heterogeneous UAV, swarm system, performance evaluation, availability dependability capability-back propagation (ADC-BP) neural network, hybrid model

中图分类号:

TP391.9

卢元杰,龙珊珊,赵航等 . 基于混合模型的异构无人机蜂群效能评估[J]. 系统仿真学报, 2024, 36(3): 700-712.

Lu Yuanjie,Long Shanshan,Zhao Hang,et al . Effectiveness Evaluation of Heterogeneous UAV Swarms Based on a Hybrid Model[J]. Journal of System Simulation, 2024, 36(3): 700-712.

图/表 13

图1

图2

表1

无人机蜂群能力指标计算模型

指标		内涵	模型
通用平台能力 B	飞行能力B₁	飞行速度(v)、飞行高度(H)、飞行距离(D)、飞行时间(t)和载荷(W_load)受限于相应门槛值(用上标^表示)	$B 1 = β M a v v ̂ + β H m a x H m a x H ̂ m a x + β d D D ̂ + β t t t ̂ + β W l o a d W l o a d W ̂ l o a d + β H H m i n H ̂ m i n - 1$
	经济可承受能力B₂	平台价格(P)不应高于与之对抗的地空导弹价格(M)的50%	$B 2 = P / 0.5 M - 1$
	通信导航能力B₃	由抗干扰(b₁)、加密通信(b₂)、导航装置(b₃)决定。其中，b_i 由无人机所装载的通信导航技术对应的评分决定	$B 3 = ∑ i = 1 3 ω b i b i$
	生存能力B₄	平台需要具备低可探测性，与翼展(b)、机身长度(l)、雷达反射截面积(RCS)相关^[21]	$B 4 = b^b l^l R C S^R C S 0.0625$
	环境适应能力B₅	多种环境条件下的使用能力，由温度(T)，风速(v_wind)、电磁环境(E_e)的影响程度决定	$B 5 = ω T T + ω V w i n d v w i n d + ω E e E e$
系统级能力 S	返航率S₁	可回收无人机(N_recycle)的百分比	$S 1 = N r e c y c l e N × 100 %$
	作战行动能力S₂	单位时间内的出动架次，与出动架次(N_launch)和发射时间(t_launch)相关	$S 2 = N l a u n c h t l a u n c h / N l a u n c h t l a u n c h^$
	任务规划能力S₃	单位规划时间所对应的蜂群规模，与蜂群规模(N)和任务规划时间(t_plan)相关	$S 3 = N t p l a n / N t p l a n^$
	智能自主能力S₄	由智能决策(s₁)、航路规划(s₂)、协同作战(s₃)能力决定。其中，s_i 由无人机所装载的智能化技术对应的评分决定	$S 4 = ∑ i = 1 3 ω s i s i$
任务执行能力 T	侦察干扰能力T₁	与侦察探测(C_r)、压制干扰(C_j)能力相关。其中：C_r是探测距离(R)、搜索方位角(α)、发现概率(P)、雷达/红外体制衡量系数(g)，同时跟踪目标数量(m₁)，同时允许攻击目标数量(m₂)的函数^[21]；C_j是我方无人机生存架次与出动架次的比值，即无干扰时敌方对我方的目标毁伤概率( $P k 0$ )和干扰因子(λ)的函数	$T 1 = ω C r C r + ω C j C j$ $C r = R 2 4 α 360 ∘ P g (m 1 m 2) 0.05$ $C j = 1 - P k 0 (1 - λ)$
	诱饵战能力T₂	与诱骗敌方火力消耗的能力有关，是无诱骗时敌方对我方的目标毁伤概率(P_k0)和诱骗因子(λ_d)的函数	$T 2 = P k 0 (1 + λ d)$
	反辐射能力T₃	与反辐射突防能力(C_s)、毁伤能力(C_k)相关。其中：C_s由我方反辐射无人机的生存能力决定，是敌方对我方反辐射无人机杀伤概率P_k函数；C_k由无人机对目标的命中率及在命中条件下的毁伤概率来决定，是无人机弹着点实际偏离目标雷达的距离(r)，弹着点偏离目标雷达的标准偏差(δ)，以及杀伤区半径(r₀)的函数^[22]	$T 3 = ω C s C s + ω C k C k$ $C s = 1 - P k$ $C k = 1 - e x p - r 2 2 δ 2 1 - e x p - 3 r 0 r 6$
	光电察打能力T₄	与侦察探测(C_r)、毁伤能力(C_k)能力相关。其中：C_r与C_k的计算分别同前所述^[21-22]	$T 4 = ω C r C r + ω C k C k$ $C r = R 2 4 α 360 ∘ P g (m 1 m 2) 0.05$ $C k = 1 - e x p - r 2 2 δ 2 1 - e x p - 3 r 0 r 6$

表1

表2

无人机蜂群能力指标权重

第1层	权重	第2层	权重
平台级能力B	$ω B = 0.194$	B₁	$ω B 1$ =0.246
		B₂	$ω B 2$ =0.093
		B₃	$ω B 3$ =0.163
		B₄	$ω B 4$ =0.283
		B₅	$ω B 5$ =0.215
系统级能力S	$ω S = 0.225$	S₁	$ω S 1$ =0.127
		S₂	$ω S 2$ =0.281
		S₃	$ω S 3$ =0.281
		S₄	$ω S 4$ =0.311
任务执行能力T	$ω T = 0.582$	T₁	$ω T 1$ =0.216
		T₂	$ω T 2$ =0.153
		T₃	$ω T 3$ =0.284
		T₄	$ω T 4$ =0.347

表2

表3

图3

图4

表4

表5

图5

图6

图7

图8

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能力	第1组评估值	第2组评估值	第3组评估值
B₁	0.795 1	0.567 7	0.276 2
B₂	0.961 6	0.422 3	0.390 0
B₃	0.944 6	0.632 8	0.247 6
B₄	0.882 5	0.546 0	0.153 3
B₅	0.782 8	0.462 4	0.263 2
S₁	0.825 7	0.663 4	0.349 3
S₂	0.881 0	0.622 2	0.285 1
S₃	0.748 9	0.599 9	0.358 8
S₄	0.814 1	0.471 4	0.280 7
T₁	0.944 7	0.463 9	0.110 4
T₂	0.837 6	0.635 3	0.258 2
T₃	0.806 9	0.560 4	0.316 8
T₄	0.784 3	0.418 5	0.343 6

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