A Novel Refined Track Initiation Algorithm for Group Targets Based on Group Model  

作　　者：GAO Fei REN He WANG Jun Amir Hussain Tariq S. Durrani 

机构地区：[1]School of Electronic and Information Engineering, Beihang University [2]School of Natural Sciences, University of Stirling [3]Department of Electronic & Electrical Engineering, University of Strathclyde

出　　处：《Chinese Journal of Electronics》2014年第4期851-856,共6页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61071139,No.60702011);the Foundation of ATR Key Lab,the Fundamental Research Funds for the Central Universities,"New Star in Blue Sky"Program Foundation,and the Royal Society of Edinburgh(RSE);the National Natural Science Foundation of China(NNSFC)under the RSE-NNSFC Joint Project(2012–2014)(No.61211130309)with the University of Stirling,Scotland,SK

摘　　要：Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that,compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that, compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.

关 键 词：Group targets Track initiation Group model State equation Data association. 

分 类 号：TN966[电子电信—信号与信息处理]