CAJ | 학술논문

针对在多帧数据条件下的目标分群问题,提出一种基于数据流聚类的动态目标分群算法TG-Stream,该算法由在线和离线2个部分组成.在线部分采用临时存储结构(TSS)和金字塔时间框架保存侦察数据集的概要信息,离线部分采用CNM算法对时间框架的信息进行聚类,最终得到分群的结果.实验结果表明,TG-Stream具有灵活的精度和效率平衡性,能较好地满足决策辅助系统处理实时信息的需要.
침대재다정수거조건하적목표분군문제,제출일충기우수거류취류적동태목표분군산법TG-Stream,해산법유재선화리선2개부분조성.재선부분채용림시존저결구(TSS)화금자탑시간광가보존정찰수거집적개요신식,리선부분채용CNM산법대시간광가적신식진행취류,최종득도분군적결과.실험결과표명,TG-Stream구유령활적정도화효솔평형성,능교호지만족결책보조계통처리실시신식적수요.
In order to solve the target grouping below multi-frame data, this paper introduces a new grouping algorithm, TG-Stream, which can be divided into two parts: on-line part and off-line part. In on-line part, it uses the concepts of a pyramidal time frame and a Temporary Storage Structure(TSS) to save summary information of sensor data. In off-line part, it uses CNM algorithm to cluster the suitable data and output the grouping result. Experimental results show that TG-Stream algorithm has good equilibrium between accuracy and efficiency.