变权区间数Heronian算子下集群态势觉察一致性评估
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摘要
无人机集群协同态势觉察一致性是集群协同作战获取信息优势的重要条件,基于此提出了基于变权区间数Heronian算子的集群态势觉察一致性评估方法,用于多种(作战仿真)情况下集群协同态势觉察一致性分析。首先,结合集群协同作战需求,从完备性、准确性等方面对一致性评估指标进行建模;然后,考虑多时刻指标数据的不确定性,构建非线性处理的区间决策矩阵,利用变权理论求取权重;最后,考虑指标的关联性,使用区间数加权Heronian平均算子集结指标数据,利用集结的区间数表征集群态势觉察一致性。结果表明,所提指标及方法可以有效分析集群协同态势觉察一致性。
Unmanned aerial vehicle(UAV)swarm cooperative situation perception consensus plays a key role in swarm cooperative combat obtaining information superiority.A consensus evaluation method of swarm cooperative situation perception via interval-number Heronian operators with variable weights is given to analyze the situation perception consensus under different combat simulation conditions.Firstly,combined with the pursuits of swarm cooperative engagement,the swarm cooperative situation perception consensus analysis indexes are built from the completeness,correctness,and so on.Then,combined with the uncertainty of multi-period data,the nonlinear dispose decision matrix and the variable weights are obtained.Finally,combined with the indexes relevance,the index data are aggregated by interval-number weighted Heronian mean operators,which could represent the situation perception consensus.The results show that the indexes and method could effectively analyze the swarm cooperative situation perception consensus.
Unmanned aerial vehicle(UAV)swarm cooperative situation perception consensus plays a key role in swarm cooperative combat obtaining information superiority.A consensus evaluation method of swarm cooperative situation perception via interval-number Heronian operators with variable weights is given to analyze the situation perception consensus under different combat simulation conditions.Firstly,combined with the pursuits of swarm cooperative engagement,the swarm cooperative situation perception consensus analysis indexes are built from the completeness,correctness,and so on.Then,combined with the uncertainty of multi-period data,the nonlinear dispose decision matrix and the variable weights are obtained.Finally,combined with the indexes relevance,the index data are aggregated by interval-number weighted Heronian mean operators,which could represent the situation perception consensus.The results show that the indexes and method could effectively analyze the swarm cooperative situation perception consensus.
引文
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[2]WALTON P C,MARIA B,JORG F.The unmanned aerial vehicle routing and trajectory optimization problem,a taxonomic review[J].Computers&Industrial Engineering,2018,120(6):116-128.
[3]ZENG Y,ZHANG R,LIM T J.Wireless communications with unmanned aerial vehicles:opportunities and challenges[J].IEEE Communications Magazine,2016,54(5):36-42.
[4]D'ANIELLO G,LOIA V,ORCIUOLI F.A multi-agent fuzzy consensus model in a situation awareness framework[J].Applied Soft Computing,2015,30:430-440.
[5]张东戈,孟辉,赵慧赟.态势感知水平的解析化度量模型[J].系统工程与电子技术,2016,38(8):1808-1815.ZHANG D G,MENG H,ZHAO H Y.Analytical measurement model of situation awareness[J].System Engineering and Electronics,2016,38(8):1808-1815.
[6]高杨,李东生,程泽新.无人机分布式集群态势感知模型研究[J].电子与信息学报,2018,40(6):1271-1278.GAO Y,LI D S,CHENG Z X.UAV distributed swarm situation awareness model[J].Journal of Electronics&Information Technology,2018,40(6):1271-1278.
[7]ENDSLEY M R.Situation awareness misconceptions and misunderstandings[J].Journal of Cognitive Engineering and Decision Making,2015,9(1):4-32.
[8]STANTON N A,SALMON P M,WALKER G H,et al.Stateof-science:situation awareness in individuals,teams and systems[J].Ergonomics,2017,60(4):449-466.
[9]罗爱民,刘俊先,刘刚.基于SEA的战场态势一致性评估方法[J].火力与指挥控制,2013,38(2):40-44.LUO A M,LIU J X,LIU G.An evaluation method of situation consistency based on SEA[J].Fire Control&Command Control,2013,38(2):40-44.
[10]ARAZY O,KOPAK R,HADAR I.Heuristic principles anddifferential judgments in the assessment of information quality[J].Journal of the Association for Information Systems,2017,18(5):403-432.
[11]房坚,王钺,袁坚.基于集合距离的信息优势度量方法[J].系统工程与电子技术,2017,39(1):114-119.FANG J,WANG Y,YUAN J.Measurement of information superiority based on set distance[J].System Engineering and Electronics,2017,39(1):114-119.
[12]施慧杰,顾浩,杜阳华.海战场态势感知能力的度量方法研究[J].指挥控制与仿真,2010,32(3):28-31.SHI H J,GU H,DU Y H.Research on measurement of sea battlefield situation awareness[J].Command Control&Simulation,2010,32(3):28-31.
[13]张才坤,朱战霞,冯琦,等.基于区间数雷达图的可视化空战威胁评估[J].系统工程与电子技术,2016,38(5):1052-1058.ZHANG C K,ZHU Z X,FENG Q,et al.Visualization threat assessment for air combat based on interval-radar chart[J].System Engineering and Electronics,2016,38(5):1052-1058.
[14]余高锋,李登峰,叶银芳,等.考虑后悔规避的异质多属性变权决策方法[J].计算机集成制造系统,2017,23(1):154-161.YU G F,LI D F YE Y F,et al.Heterogeneous multi-attribute variable weight decision making method considering regret aversion[J].Computer Integrated Manufacturing Systems,2017,23(1):154-161.
[15]LIU P,SHI L.Some neutrosophic uncertain linguistic number Heronian mean operators and their application to multi-attribute group decision making[J].Neural Computing and Applications,2017,28(5):1079-1093.
[16]LIU P,CHEN S M,LIU J.Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators[J].Information Sciences,2017,411:98-121.
[17]汪培庄.模糊集与随机集落影[M].北京:北京师范大学出版社,1985.WANG P Z.Fuzzy sets and the falling shadow of random sets[M].Beijing:Beijing Normal University Press,1985.
[18]LLAMAZARES B,PENA T.Aggregating preference ranking with variable weights[J].European Journal of Operational Research,2013,230(2):348-355.
[19]BELIAKOV G,PRADERA A,CALVO T.Aggregation functions:aguide for practitioners[M].Berlin:Springer,2007.
[20]YU D J.Intuitionistic fuzzy geometric Heronian mean aggregation operators[J].Applied Soft Computing,2013,13(2):1235-1246.