ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Dynamic situation assessment method of aerial warfare based on improved evidence network
Received date: 2015-02-06
Revised date: 2015-05-04
Online published: 2015-12-28
Supported by
National Natural Science Foundation of China (61374032); Aeronautical Science Foundation of China (2012ZA01011); General Science Research Project of Department of Education of Liaoning (L2015412)
Aimed at the comprehensive consideration for the impact types and the need of reasoning ability with uncertainty that situation assessment of UAV aerial warfare requires, a model of dynamic situation assessment based on improved evidence network is established and the threat level evaluation reasoning method is designed. Firstly, considering the characteristics of short decision time, a grade reduction method for variable recognition frame is proposed to improve the network operation efficiency. Then according to the characteristics of large uncertainties of aerial warfare situation information, the adaptive combination arithmetic of conflicting data and the time series prediction for evidence are added to advance the rationality of evidence. Finally, the temporal-spatial fusion thought and the variable weight mechanism are also introduced to make the threat information of previous time an important standard for the threat assessment of the next time. Due to the threat recursion combination in the temporal direction, the threat information transmission is increased. It is verified that the problem of irrational assessment results caused by the distortion of information is improved and the proposed method is effectively validated by the simulation examples.
WANG Yu , ZHANG Weiguo , FU Li , HUANG Degang , LI Yong . Dynamic situation assessment method of aerial warfare based on improved evidence network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015 , 36(12) : 3896 -3909 . DOI: 10.7527/S1000-6893.2015.0117
[1] Narayana R P, Sudesh K K, Girija G. Situation assessment in air-combat:A fuzzy-bayesian hybrid approach[C]//Proceedings of the International Conference on Aerospace Science and Technology, 2008, 63-68.
[2] Dong Y F, Guo J L, Zhang H X. Threat assessment for multi-aircraft-air combat[J]. Fire Control & Command Control, 2002, 27(4):73-76(in Chinese).董彦非,郭基联,张恒喜.多机空战目标威胁评估算法[J].火力与指挥控制, 2002, 27(4):73-76.
[3] Zhang K, Zhou D Y. TOPSIS method based on entropy in evaluating the air multi-target threat[J]. Systems Engineering and Electronics, 2007, 29(9):1493-1495(in Chinese).张堃,周德云.基于熵的TOPSIS法空战多目标威胁评估[J].系统工程与电子技术, 2007, 29(9):1493-1495.
[4] Wu W H, Zhou S Y, Gao L, et al. Improvements of situation assessment for beyond-visual-range air combat based on missile launching envelope analysis[J]. Systems Engineering and Electronics, 2011, 33(12):2679-2684(in Chinese).吴文海,周思羽,高丽,等.基于导弹攻击区的超视距空战态势评估改进[J].系统工程与电子技术, 2011, 33(12):2679-2684.
[5] de Campos L M, Acid S. A hybrid methodology for learning Bayesian networks:Benedict[J]. International Journal of Approximate Reasoning, 2001, 27(3):235-262.
[6] Lei Y J, Wang B S, Wang Y. Techniques for battlefield situation assessment based on intuitionistic fuzzy decision[J]. Acta Electronica Sinica, 2006, 34(12):2175-2179(in Chinese).雷英杰,王宝树,王毅.基于直觉模糊决策的战场态势评估方法[J].电子学报, 2006, 34(12):2175-2179.
[7] Lu J, Zhang G, Ruan D. Intelligent multi-criteria fuzzy group decision-making for situation assessments[J]. Soft Computing, 2008, 12(3):289-299.
[8] Steinberg A N. An approach to threat assessment[M]. Springer Netherlands:Harbour Protection Through Data Fusion Technologies, 2009:95-108.
[9] Cai J, Hu J, Huang C Q. A consistent dominance rough sets method and its application in threat assessment of UCAV's targets[J]. Systems Engineering-Theory & Practice, 2012, 32(6):1377-1384(in Chinese).蔡佳,胡杰,黄长强.协调优势粗糙集方法及其在UCAV目标威胁估计中的应用[J].系统工程理论与实践, 2012, 32(6):1377-1384.
[10] Wang J J, Shi K Q, Lei Y J. Method of situation forecast based on function S-rough sets[J]. Systems Engineering and Electronics, 2007, 29(2):214-216(in Chinese).王晶晶,史开泉,雷英杰.一种基于函数S-粗集的态势预测方法[J].系统工程与电子技术, 2007, 29(2):214-216.
[11] Wang X, Wang B. Methods for situation assessment of multi-attribute decision making based on rough sets[C]//Proceedings of 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. Piscataway, NJ:IEEE Press, 2009:325-328.
[12] Gao X G, Chen H Y, Shi J G. Study on the mechanism of structure-variable dynamic Bayesian networks[J]. Acta Automatica Sinica, 2011, 37(12):1435-1444(in Chinese).高晓光,陈海洋,史建国.变结构动态贝叶斯网络的机制研究[J].自动化学报, 2011, 37(12):1435-1444.
[13] Wang X F, Wang B S. Situation assessment method based on Bayesian network and intuitionistic fuzzy reasoning[J]. Systems Engineering and Electronics, 2009, 31(11):2742-2746(in Chinese).王晓帆,王宝树.基于贝叶斯网络和直觉模糊推理的态势估计方法[J].系统工程与电子技术, 2009, 31(11):2742-2746.
[14] Mirmoeini F, Krishnamurthy V. Reconfigurable Bayesian networks for adaptive situation assessment in battlespace[C]//Proceedings of IEEE International Conference on Networking, Sensing and Control. Piscataway, NJ:IEEE Press, 2005:810-815.
[15] Deng Y, Zhu Z F, Zhong S. Fuzzy information fusion based on evidence theory and its application in target recognition[J]. Acta Aeronautica et Astronautica Sinica, 2005, 26(6):754-758(in Chinese).邓勇,朱振福,钟山.基于证据理论的模糊信息融合及其在目标识别中的应用[J].航空学报, 2005, 26(6):754-758.
[16] Huang S, Su X, Hu Y, et al. A new decision-making method by incomplete preferences based on evidence distance[J]. Knowledge-Based Systems, 2014, 56(3):264-272.
[17] Hewawasam K K R, Premaratne K, Shyu M L, et al. Rule mining and classification in a situation assessment application:A belief-theoretic approach for handling data imperfections[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2007, 37(6):1446-1459.
[18] Jiang J. Modeling, reasoning and learning approach to evidential network[D]. Changsha:National University of Defense Technology, 2011(in Chinese).姜江.证据网络建模,推理及学习方法研究[D].长沙:国防科学技术大学, 2011.
[19] Benavoli A, Ristic B, Farina A, et al. An application of evidential networks to threat assessment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2):620-639.
[20] Ristic B, Smets P. Target identification using belief functions and implication rules[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3):1097-1103.
[21] Liu M H, Xiao D Y. Multi-sensor data fusion based on similitude degree[J]. Control and Decision, 2004, 19(5):534-537(in Chinese).刘敏华,萧德云.基于相似度的多传感器数据融合[J].控制与决策, 2004, 19(5):534-537.
[22] Luo D Y, Zhang Y. Research of spatial-temporal architecture model and the algorithm for multisensor information fusion[J]. Systems Engineering and Electronics, 2004, 26(1):36-39(in Chinese).罗大庸,张远.多传感器信息时空融合模型及算法研究[J].系统工程与电子技术, 2004, 26(1):36-39.
[23] Liu Z G, Cheng Y M, Pan Q, et al. Target identification by adaptive combination of conflicting evidence[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(7):1426-1432(in Chinese).刘准钆,程咏梅,潘泉,等.证据冲突下自适应融合目标识别算法[J].航空学报, 2010, 31(7):1426-1432.
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