Study on Tracking Strong Maneuvering Targets Based on IMM-GMPHDA

被引：0
|
作者：
Ding, Hai-Long [1 ]
Zhao, Wen-Bo [1 ]
Zhang, Luo-Zheng [1 ]
机构：
[1] Army Officer Acad PLA, Hefei 230031, Anhui, Peoples R China
来源：
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT II | 2016年 / 9772卷
关键词：
GMPHDA; Interacting multi-model; Strong maneuvering target; Multi-radar networking; HYPOTHESIS DENSITY FILTER;
D O I：
10.1007/978-3-319-42294-7_74
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Gaussian mixture probability hypothesis density filter algorithm (GMPHDA), which is effective method for tracking unknown number of multi-target in strong clutter environment, has solid theoretical basis. But it is hard to track target by GMPHDA when the targets maneuver. To model maneuvering target, we introduce interacting multi-model (IMM) in GMPHDA by modeling maneuvering model of survival target and fusing probability hypothesis density of each model filter based on latest model probability, getting IMM-GMPHDA. The simulation results show that we can real-time track strong maneuvering and supersonic multi-target with IMM-GMPHDA, whose tracking precision can reach 70 m in multi-radar networking system, which meets the project requirement.
引用
收藏
页码：838 / 849
页数：12
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