基于组合赋权的对地攻击无人机自主能力云模型评价

严惊涛; 刘树光

doi:10.13700/j.bh.1001-5965.2022.0072

基于组合赋权的对地攻击无人机自主能力云模型评价

doi: 10.13700/j.bh.1001-5965.2022.0072

严惊涛,
刘树光^,

空军工程大学装备管理与无人机工程学院，西安 710051

详细信息

通讯作者:
E-mail：dawny418@163.com

中图分类号: V279
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出版历程
- 收稿日期: 2022-02-14
- 录用日期: 2022-04-18
- 网络出版日期: 2022-04-25
- 整期出版日期: 2023-12-31

Combination weighting based cloud model evaluation of autonomous capability of ground-attack UAV

YAN Jingtao,
LIU Shuguang^,

Equipment Management and Unmanned Aerial Vehicle Engineering College，Air Force Engineering University，Xi’an 710051，China

More Information

Corresponding author: E-mail：dawny418@163.com

摘要

摘要:
针对对地攻击无人机自主能力量化评价的不确定性问题，提出基于组合赋权的云模型评价方法。基于认知控制结构，从感知探测、规划决策、作战执行、安全管理和学习进化5个方面构建自主能力评价指标体系。运用基于博弈论的组合赋权方法，结合改进层次分析法和改进熵权法确定组合权重，克服了单一赋权方法确定指标权重的片面性。考虑自主能力评价过程的模糊性和随机性，提出一种对地攻击无人机自主能力云模型评价方法，采用浮动云算法实现评价指标云的有效综合。对3种对地攻击无人机进行仿真验证，结果表明：所提方法综合考虑评价对象的主客观因素，消除了单一赋权方法的局限性，权重分配科学合理。自主能力云模型量化评价能够有效区分不同类型对地攻击无人机自主能力等级的差异性，评价结果准确可信。
- 自主能力 /
- 对地攻击无人机 /
- 组合赋权 /
- 博弈论 /
- 云模型
Abstract:
To address the uncertainty in quantitative evaluation of autonomous capability of ground-attack UAVs, an evaluation method with the cloud model is proposed based on combined weightings. Based on the cognitive control structure, the evaluation index system of autonomous capability is constructed from five aspects: perceptual detection, planning and decision-making, combat execution, security management, and learning evolution. The one sidedness of determining the index weight by a single weighting method is overcome, using the combination weighting method based on game theory, and combined with the improved analytic hierarchy process and the improved entropy weight method to determine the combination weight. Considering the fuzziness and randomness of the autonomous capability evaluation process, an evaluation method based on cloud model is proposed for the autonomous capability of ground-attack UAVs, and the floating cloud algorithm is used to realize the effective synthesis of the evaluation index cloud. The simulation results of three ground-attack UAVs show that the proposed method considers both subjective and objective factors of the evaluation object, eliminates the limitations of a single weighting method, and achieves scientific and reasonable weight distribution. The quantitative evaluation of autonomous capability of the cloud model can effectively distinguish autonomous capability levels of different types of ground-attack UAVs, with accurate and reliable evaluation results.
- autonomous caoability /
- ground-attack UAV /
- combination weighting /
- game theory /
- cloud model

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图 1 人类/无人系统智能结构

Figure 1. Intelligent structure of human/unmanned system

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图 2 对地攻击无人机自主能力评价指标体系

Figure 2. Evaluation index system of autonomous capability of ground-attack UAV

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图 3 自主能力评价流程

Figure 3. Flow chart of evaluation of autonomous capability

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图 4 自主能力评价指标权重分布

Figure 4. Weight distribution of evaluation index of autonomous capability

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图 5 自主能力评价等级标准云图

Figure 5. Cloud chart of evaluation level standard of autonomous capability

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图 6 UAV1自主能力综合云图

Figure 6. Comprehensive cloud map of UAV1 autonomous capability

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图 7 本文方法与其他评价方法对比

Figure 7. Comparison with other evaluation methods

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图 8 指标与综合评价结果相似度分布

Figure 8. Similarity distribution of between indicators and comprehensive evaluation results

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图 9 指标与综合评价结果平均相似度分布

Figure 9. Average similarity distribution between indicators and comprehensive evaluation results

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表 1 自主能力等级划分标准

Table 1. Classification standard of autonomous capability

等级及分值区间	等级描述	感知探测能力	规划决策能力	作战执行能力	安全管理能力	学习进化能力
Ⅰ [0,25]	单机简单计划任务	探测地面特定目标	执行预编程的规划任务	单机对地攻击	状态报告	计算、存储、数据处理
Ⅱ (25,50]	单机复杂计划任务	外部态势及自身态势感知	面向飞行状态的适应性规划	单机攻击并毁伤评估	实时故障诊断与隔离	程序自动化
Ⅲ (50,75]	单机实时规划任务	复杂环境感知	航路重规划	及时规避部分威胁	简单故障修复	计算智能、智能算法
Ⅳ (75,90]	多机任务协同	多机信息共享	长机分配战术决策	多机协助攻击	故障预测及容错控制	简单思维智慧
Ⅴ (90,100]	全自主集群	分布式/集群态势感知与信息共享	分布式/集群战略决策	集群协同攻击	群组诊断、冲突消解	认知/记忆智能、自主学习

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表 2 无人机基本性能参数

Table 2. Basic performance parameters of UAV

UAV	长/m	翼展/m	高/m	翼面积/m²	最大起飞重量/kg	升限/m	最高速度/(km·h⁻¹)	巡航速度/(km·h⁻¹)
UAV1	11.7	24	3.8	29.5	5670	12192	460	398
UAV2	8	17	2.1	17.8	1633	8800	280	110
UAV3	8.22	14.8	2.1	11.5	1020	7620	217	165

UAV	雷达分辨率/m	目标定位精度/m	导弹外挂数量/枚	起降距离/m	续航时间/h	有效载荷/kg	发动机功率/kW	最大航程/km
UAV1	0.1	0.10	8	600	40	1360	661.5	10186
UAV2	0.2	0.20	4	640	30	360	99.2	4800
UAV3	0.3	0.25	2	667	24	200	84.5	3704

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表 3 归一化指标量化值

Table 3. Normalized index quantization value

UAV	${B_1}$	${B_2}$	${B_3}$	${B_4}$	${B_5}$	${B_6}$	${B_7}$	${B_8}$	${B_9}$	${B_{10}}$
UAV1	0.84	0.87	0.75	0.66	0.81	0.73	0.71	0.73	0.67	0.82
UAV2	0.56	0.75	0.61	0.62	0.54	0.62	0.57	0.61	0.62	0.57
UAV3	0.40	0.62	0.43	0.42	0.43	0.46	0.46	0.55	0.47	0.43

UAV	${B_{11}}$	${B_{12}}$	${B_{13}}$	${B_{14}}$	${B_{15}}$	${B_{16}}$	${B_{17}}$	${B_{18}}$	${B_{19}}$
UAV1	0.76	0.74	0.81	0.82	0.79	0.80	0.88	0.80	0.80
UAV2	0.53	0.65	0.71	0.62	0.59	0.58	0.54	0.56	0.68
UAV3	0.38	0.52	0.60	0.44	0.46	0.41	0.44	0.45	0.50

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表 4 指标权重及UAV1云模型特征参数

Table 4. Index weight, and characteristic parameters of UAV1 cloud model

指标	主观权重	客观权重	组合权重	UAV1指标云
$ {B_1} $	0.0726	0.0948	0.0896	(62.6,2.44,0.14)
$ {B_2} $	0.0717	0.0227	0.0341	(68.4,3.38,0.96)
$ {B_3} $	0.0599	0.0564	0.0572	(55.6,2.71,1.18)
$ {B_4} $	0.0404	0.0412	0.0410	(60.0,3.38,0.79)
$ {B_5} $	0.0718	0.0734	0.0730	(62.4,6.74,2.98)
$ {B_6} $	0.0678	0.0398	0.0463	(67.8,2.76,1.68)
$ {B_7} $	0.0311	0.0315	0.0314	(63.0,2.13,1.02)
$ {B_8} $	0.0441	0.0153	0.0220	(59.6,1.74,0.85)
$ {B_9} $	0.0638	0.0288	0.0370	(66.2,4.61,2.53)
$ {B_{10}} $	0.0687	0.0754	0.0738	(60.2,3.01,0.53)
$ {B_{11}} $	0.0554	0.0865	0.0792	(64.8,1.88,1.37)
$ {B_{12}} $	0.0680	0.0231	0.0336	(72.4,1.57,0.91)
$ {B_{13}} $	0.0372	0.0154	0.0205	(56.8,5.73,1.65)
$ {B_{14}} $	0.0501	0.0697	0.0651	(59.2,2.36,0.54)
$ {B_{15}} $	0.0552	0.0543	0.0545	(57.4,1.42,0.66)
$ {B_{16}} $	0.0384	0.0715	0.0638	(63.0,1.57,0.08)
$ {B_{17}} $	0.0527	0.0986	0.0879	(66.4,1.67,0.49)
$ {B_{18}} $	0.0293	0.0671	0.0583	(64.8,2.37,0.86)
$ {B_{19}} $	0.0219	0.0345	0.0316	(69.6,1.93,1.15)

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表 5 自主能力等级标准云模型

Table 5. Cloud model of autonomous capability level standard

等级	分值区间	云模型特征参数
Ⅰ	[0,25]	（12.5,10.617,1.06）
Ⅱ	(25,50]	（37.5,10.617,1.06）
Ⅲ	(50,75]	(62.5,10.617,1.06)
Ⅳ	(75,90]	(82.5,6.370,0.64)
Ⅴ	(90,100]	(95,4.247,0.42)

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表 6 UAV1专家评分

Table 6. UAV1 expert scoring

专家	${B_1}$	${B_2}$	${B_3}$	${B_4}$	${B_5}$	${B_6}$	${B_7}$	${B_8}$	${B_9}$	${B_{10}}$	${B_{11}}$	${B_{12}}$	${B_{13}}$	${B_{14}}$	${B_{15}}$	${B_{16}}$	${B_{17}}$	${B_{18}}$	${B_{19}}$
1	62	72	57	66	60	64	65	56	66	54	71	73	47	55	59	64	67	65	73
2	64	65	60	59	73	68	63	59	54	58	65	72	52	61	56	63	68	63	68
3	59	72	51	55	54	69	60	61	71	69	65	72	61	63	59	60	70	68	67
4	67	68	56	57	64	68	64	60	75	60	66	75	62	60	57	66	74	62	71
5	64	70	56	58	60	71	62	57	62	59	64	70	63	58	58	64	64	64	69
6	58	71	54	64	56	63	60	59	62	54	62	72	55	57	57	63	63	63	70
7	60	66	61	62	58	68	70	63	65	62	64	76	56	60	58	63	66	65	70
8	60	65	55	63	58	65	64	61	68	63	64	74	42	62	57	61	67	66	70
9	64	71	56	61	55	68	62	61	65	60	65	71	62	56	56	64	69	69	69
10	63	63	54	55	70	72	63	60	64	59	63	72	62	61	57	65	70	68	67
11	64	70	53	58	69	70	60	59	69	59	68	72	54	56	55	62	62	62	67
12	64	70	57	63	65	70	65	59	65	63	65	70	62	59	60	64	64	64	69
13	62	67	58	59	66	65	62	58	65	62	65	73	59	60	56	62	65	63	72
14	75	72	54	59	58	65	62	61	67	61	62	75	58	59	59	61	67	67	69
15	63	65	52	61	70	71	63	60	75	60	63	69	57	61	57	63	61	63	73

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表 7 相似度及等级评定结果

Table 7. Similarity and grade evaluation results

UAV	自相似度					评定结果
UAV	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	本文方法	灰色关联	贝叶斯网络
UAV1	0	0.07	0.96	0.02	0	Ⅲ	Ⅲ	Ⅲ
UAV2	0.21	0.82	0.02	0	0	Ⅱ	Ⅱ	Ⅱ
UAV3	0.76	0.23	0.01	0	0	Ⅰ	Ⅰ	Ⅰ

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