基于累积前景理论和三支决策的无人机态势评估

doi:10.16183/j.cnki.jsjtu.2021.400

摘要/Abstract

摘要：

现有的无人机态势评估方法没有考虑复杂外界环境对决策者的影响,并且通常只能得到评估的排序结果,决策者需要在短时间内进行决策,常出现误判或错失打击窗口期.针对该问题,提出一种基于累积前景理论的三支决策模型.首先,通过直觉模糊接近于理想值的排序方法求取目标的条件概率, 得到态势评估结果;之后基于累积前景理论对无人机获取的直觉模糊态势信息进行计算,得到每个目标执行不同动作时对应的累积前景价值;最后,基于累积前景价值最大化的原则,推导出新的三支决策规则,对态势评估结果进行三支划分.实验分析表明:该方法不仅得到了目标威胁排序,而且对目标威胁等级进行客观分类;同时在评估过程中考虑决策者的心理,得到符合决策者特质的目标威胁评估结果,为复杂多变的空战提供一种合理的决策支持.

关键词: 无人机, 三支决策, 累积前景理论, 态势评估, 直觉模糊

Abstract:

General uninhabited aerial vehicle (UAV) situation assessment methods do not consider the influence of complex external environment on the decision-maker, and usually only get the ranking results of the evaluation. Since the decision-maker needs to make decisions in a short period of time, misjudgments or missing strike windows often occur. To address this problem, a three-way decision model based on the cumulative prospect theory is proposed. First, the method the utilizes intuitionistic fuzzy technique for order preference by similarity to an ideal solution to estimate the conditional probability of each target and obtains the situation assessment result. Next, the method calculates the intuitionistic fuzzy situation information obtained by the UAV based on the cumulative prospect theory, and obtains the corresponding cumulative prospect value when each target performs different actions. Finally, based on the principle of maximizing the cumulative prospect value, a new three-way decision rule is derived to divide the situational assessment results into three regions. The experimental analysis shows that the method not only obtains the target threat ranking, but also classifies the target threat level objectively. At the same time, it considers the psychology of the decision-maker in the assessment process, and obtains the target threat assessment results that meet the traits of the decision-maker, providing a reasonable decision support for the complex and changing air combat.

Key words: uninhabited aerial vehicle (UAV), three-way decision, cumulative prospect theory, situation assessment, intuitionistic

中图分类号:

V279

李伟湋, 高培雪, 陈进, 路玉卿. 基于累积前景理论和三支决策的无人机态势评估[J]. 上海交通大学学报, 2022, 56(11): 1479-1490.

LI Weiwei, GAO Peixue, CHEN Jin, LU Yuqing. Unmanned Aerial Vehicle Situation Assessment Based on Cumulative Prospect Theory and Three-Way Decision[J]. Journal of Shanghai Jiao Tong University, 2022, 56(11): 1479-1490.

图/表 15

表1

表2

表3

表4

表5

表6

表7

实验结果对比

方法类型	排序结果	分类结果
本文方法	T₁>T₃>T₂>T₄>T₅	$P O S (C) = {T 1} B N D (C) = {T 3, T 2} N E G (C) = {T 4, T 5}$
VIKOR^[7]	T₃>T₁>T₄>T₂>T₅	无
三支决策^[8]	T₁>T₃>T₂>T₄>T₅	$P O S (C) = {T 1, T 3} B N D (C) = {T 2} N E G (C) = {T 4, T 5}$
IFSMRM^[16]	T₃>T₁>T₂>T₄>T₅	无

表7

图1

图2

表8

表9

表10

3种类型决策者的结果

飞行员类型	排序结果	分类结果
冒险型	T₁>T₃>T₂>T₄>T₅	$P O S (C) = {T 1, T 3, T 2} N E G (C) = {T 4, T 5}$
中间型	T₁>T₃>T₂>T₄>T₅	$P O S (C) = {T 1} B N D (C) = {T 3, T 2} N E G (C) = {T 4, T 5}$
保守型	T₁>T₃>T₂>T₄>T₅	$P O S (C) = {T 1} B N D (C) = {T 3, T 2} N E G (C) = {T 4, T 5}$

表10

图3

图4

图5

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动作	C	?C
a_P	λ_PP	λ_PN
a_B	λ_BP	λ_BN
a_N	λ_NP	λ_NN

目标	T₁	T₂	T₃	T₄	T₅
类型	(0.7, 0.2)	(0.7, 0.0)	(0.5, 0.1)	(0.5, 0.3)	(0.1, 0.6)
距离	(0.235, 0.702)	(0.281, 0.662)	(0.194, 0.747)	(0.203, 0.759)	(0.726, 0.232)
角度	(0.446, 0.272)	(0.279, 0.515)	(0.223, 0.511)	(0.248, 0.584)	(0.446, 0.272)
速度	(0.546, 0.193)	(0.136, 0.718)	(0.579, 0.274)	(0.239, 0.637)	(0.568, 0.903)
高度	(0.517, 0.167)	(0.346, 0.501)	(0.041, 0.917)	(0.172, 0.824)	(0.517, 0.286)
干扰能力	(0.5, 0.1)	(0.7, 0.0)	(0.8, 0.1)	(0.2, 0.5)	(0.0, 0.6)
穿透能力	(0.8, 0.1)	(0.3, 0.4)	(0.8, 0.0)	(0.3, 0.1)	(0.3, 0.5)
防护能力	(0.6, 0.3)	(0.6, 0.1)	(0.8, 0.1)	(0.6, 0.3)	(0.2, 0.7)

权重类型	T₁	T₂	T₃	T₄	T₅
w⁺(P_r(C\|T_i)	0.5633	0.4420	0.4856	0.3399	0.3221
w^-(P_r(C\|T_i)	0.6209	0.4799	0.5320	0.3542	0.3322
w⁺(P_r(?C\|T_i)	0.2947	0.3997	0.3595	0.5081	0.5293
w^-(P_r(?C\|T_i)	0.2983	0.4283	0.3786	0.5583	0.5826

目标	动作	σ=0.35
目标	动作	C	?C
T₁	a_P	0.2610	-0.4570
	a_B	-0.2610	-0.1600
	a_N	-0.7548	0.1600
T₂	a_P	0.2233	-0.5823
	a_B	-0.2233	-0.2038
	a_N	-0.6380	0.2038
T₃	a_P	0.2297	-0.5079
	a_B	-0.2297	-0.1778
	a_N	-0.6564	0.1778
T₄	a_P	0.1748	-0.6923
	a_B	-0.1748	-0.2423
	a_N	-0.4995	0.2423
T₅	a_P	0.1709	-0.7054
	a_B	-0.1709	-0.2469
	a_N	-0.4884	0.2469

累积前景价值类型	T₁	T₂	T₃	T₄	T₅
V_P	0.0480	-0.2439	-0.1077	-0.5721	-0.6275
V_B	-0.1589	-0.1336	-0.1067	-0.1251	-0.1319
V_N	-0.7560	-0.3817	-0.4889	-0.0637	-0.0246