Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每...为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每一棵决策树视为一个基聚类器,根据基聚类器集合的稳定和不稳定性设计两种不同的聚类集成选择方法,将评估单个决策树对随机森林的增益问题,转化为基聚类器对最终的聚类集成结果的增益问题。该算法与5种对比方法在10个数据集上进行比较,实验结果验证了RFCCES的独特优势和整体有效性。展开更多
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘为解决一些决策树受到数据噪声等因素的影响,导致它们对随机森林聚类产生有限甚至负面贡献这一问题,提出一种基于聚类集成选择的随机森林聚类方法(random forest clustering method based on cluster ensemble selection,RFCCES)。将每一棵决策树视为一个基聚类器,根据基聚类器集合的稳定和不稳定性设计两种不同的聚类集成选择方法,将评估单个决策树对随机森林的增益问题,转化为基聚类器对最终的聚类集成结果的增益问题。该算法与5种对比方法在10个数据集上进行比较,实验结果验证了RFCCES的独特优势和整体有效性。
