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Research of maneuvering target prediction and tracking technology based on IMM algorithm
Cao, Zheng1,2,3; Mao, Yao1,2; Deng, Chao1,2,3; Liu, Qiong1,2; Chen, Jing1,2,3
2016
发表期刊Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment
ISSN0277-787X
卷号9684页码:968430
文章类型C
摘要Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-Tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms. © 2016 SPIE.
关键词Ballistics Fighter Aircraft Forecasting Manufacture Markov Processes Military Applications Optical Testing Target Tracking Tracking (Position) Trajectories Unmanned Aerial Vehicles (Uav) Vehicles
DOI10.1117/12.2243242
收录类别SCI ; Ei
语种英语
项目资助者Chinese Academy of Sciences, Institute of Optics and Electronics (IOE) ; The Chinese Optical Society (COS)
WOS记录号WOS:000387429500108
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ioe.ac.cn/handle/181551/8552
专题光电工程总体研究室(一室)
作者单位1. Institute of Optics and Electronics, Chinese Academy of Science, Chengdu
2.610209, China
3. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu
4.610209, China
5. University of Chinese Academy of Sciences, Beijing
6.100039, China
推荐引用方式
GB/T 7714
Cao, Zheng,Mao, Yao,Deng, Chao,et al. Research of maneuvering target prediction and tracking technology based on IMM algorithm[J]. Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment,2016,9684:968430.
APA Cao, Zheng,Mao, Yao,Deng, Chao,Liu, Qiong,&Chen, Jing.(2016).Research of maneuvering target prediction and tracking technology based on IMM algorithm.Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment,9684,968430.
MLA Cao, Zheng,et al."Research of maneuvering target prediction and tracking technology based on IMM algorithm".Proceedings of SPIE: 8th International Symposium on Advanced Optical Manufacturing and Testing Technology: Optical Test, Measurement Technology, and Equipment 9684(2016):968430.
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