航母编队反潜目标识别和威胁评估仿真
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摘要
采用贝叶斯网络模型对目标识别、威胁评估是一种有效的定量分析方法。这里首次将贝叶斯网络模型运用到航母编队作战决策中识别水下目标和评估威胁等级。结合部队实际情况分别构建目标识别和威胁评估贝叶斯网络模型;基于部队实践数据、院校专家和查阅资料构建符合实际情况的条件概率表;最后通过仿真实验对水下目标进行识别和评估威胁等级,对比部队相关数据验证了贝叶斯网络对航母编队目标识别和威胁评估的有效性,能够为航母编队指挥员反潜作战提供一定的辅助决策。
Bayesian network model is a kind of effective quantitative analysis method for target recognition and threat assessment. The Bayesian network model is used to identify underwater targets and assess threat levels in aircraft carrier battle operations for the first time. Firstly,the bayesian network model of target recognition and threat assessment is constructed according to the actual situation of the troops;Secondly,the condition probability table is constructed based on the force practice data,the institution expert and the access information,which conforms to the actual situation;Finally,the underwater targets are identified and threat level is evaluated by simulation experiments,the validity of the Bayesian network on the target identification and threat assessment of the aircraft carrier formation is verified by the relevant data of the comparative forces,which can provide some auxiliary decision for the anti-submarine warfare of the aircraft carrier formation commander.
Bayesian network model is a kind of effective quantitative analysis method for target recognition and threat assessment. The Bayesian network model is used to identify underwater targets and assess threat levels in aircraft carrier battle operations for the first time. Firstly,the bayesian network model of target recognition and threat assessment is constructed according to the actual situation of the troops;Secondly,the condition probability table is constructed based on the force practice data,the institution expert and the access information,which conforms to the actual situation;Finally,the underwater targets are identified and threat level is evaluated by simulation experiments,the validity of the Bayesian network on the target identification and threat assessment of the aircraft carrier formation is verified by the relevant data of the comparative forces,which can provide some auxiliary decision for the anti-submarine warfare of the aircraft carrier formation commander.
引文
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[6]翟贵敏,董龙明,邱瑞波,等.基于贝叶斯网络的空中目标威胁估计算法[J].火力与指挥控制,2016,41(11):90-93.
[7]周晓光,张佳利,何晓蕾,等.基于预定作业周期的航母作战部署调度研究[J].火力与指挥控制,2016,41(8):70-74.
[8]孟庆操,杨光.航母编队防空作战编成评判方法[J].火力与指挥控制,2017,42(6):51-55.
[9]付涛,王军.防空系统中空中目标威胁评估方法研究[J].指挥控制与仿真,2016,38(3):63-69.
[10]夏命辉,王小平,林秦颖,等.复杂环境下基于动态贝叶斯网络的目标识别[J].空军工程大学学报(自然科学版),2016,17(4):24-28.
[2]BEAVER J M,KEREKES R A,TREADWELL J N. An information fusion framework for threat assessment[C]//International Conference on Information Fusion. IEEE,2009:1903-1910.
[3]张银燕,李弼程.基于MIN-MAX云重心推理的目标威胁评估方法[J].系统仿真学报,2014,26(2):411-418.
[4]DUBEY D,CHANDRA S,MEHRA A. Fuzzy linear programming under interval uncertainty based on IFS representation[J].Fuzzy Sets&Systems,2012,188(1):68-87.
[5]王巍.基于云参数贝叶斯网络的威胁评估方法[J].计算机技术与发展,2016,26(6):106-110.
[6]翟贵敏,董龙明,邱瑞波,等.基于贝叶斯网络的空中目标威胁估计算法[J].火力与指挥控制,2016,41(11):90-93.
[7]周晓光,张佳利,何晓蕾,等.基于预定作业周期的航母作战部署调度研究[J].火力与指挥控制,2016,41(8):70-74.
[8]孟庆操,杨光.航母编队防空作战编成评判方法[J].火力与指挥控制,2017,42(6):51-55.
[9]付涛,王军.防空系统中空中目标威胁评估方法研究[J].指挥控制与仿真,2016,38(3):63-69.
[10]夏命辉,王小平,林秦颖,等.复杂环境下基于动态贝叶斯网络的目标识别[J].空军工程大学学报(自然科学版),2016,17(4):24-28.