UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ  

作　　者：XIE Lei TANG Shangqin WEI Zhenglei XUAN Yongbo WANG Xiaofei 

机构地区：[1]Institute of Aeronautics Engineering,Air Force Engineering University,Xi’an 710038,China [2]China Aerodynamics Research&Development Center,Mianyang 621000,China [3]Blue Sky Innovation Center for Frontier Science,Beijing 100000,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第5期1235-1251,共17页系统工程与电子技术（英文版）

基　　金：supported by the Natural Science Foundation of Shaanxi Province(2020JQ-481,2021JM-224);the Aeronautical Science Foundation of China(201951096002).

摘　　要：The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.

关 键 词：unmanned combat aerial vehicle(UCAV) situation assessment clustering K-MEANS stacked autoencoder learn-ing vector quantization 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP311.13[自动化与计算机技术—计算机软件与理论]