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Dim moving target tracking algorithm based on particle discriminative sparse representation
Li, Zhengzhou1,2; Li, Jianing1; Ge, Fengzeng1; Shao, Wanxing1; Liu, Bing1; Jin, Gang2,3
Source PublicationINFRARED PHYSICS & TECHNOLOGY
Volume75Pages:100-106
2016-03-01
Language英语
ISSN1350-4495
DOI10.1016/j.infrared.2016.01.008
Indexed BySCI
WOS IDWOS:000371555800015
SubtypeArticle
AbstractThe small dim moving target usually submerged in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio (SNR). A target tracking algorithm based on particle filter and discriminative sparse representation is proposed in this paper to cope with the uncertainty of dim moving target tracking. The weight of every particle is the crucial factor to ensuring the accuracy of dim target tracking for particle filter (PF) that can achieve excellent performance even under the situation of non-linear and non-Gaussian motion. In discriminative over-complete dictionary constructed according to image sequence, the target dictionary describes target signal and the background dictionary embeds background clutter. The difference between target particle and background particle is enhanced to a great extent, and the weight of every particle is then measured by means of the residual after reconstruction using the prescribed number of target atoms and their corresponding coefficients. The movement state of dim moving target is then estimated and finally tracked by these weighted particles. Meanwhile, the subspace of over-complete dictionary is updated online by the stochastic estimation algorithm. Some experiments are induced and the experimental results show the proposed algorithm could improve the performance of moving target tracking by enhancing the consistency between the posteriori probability distribution and the moving target state. (C) 2016 Elsevier B.V. All rights reserved.
KeywordDim Target Tracking Particle Weight Estimation Discriminative Sparse Representation Posteriori Probability Distribution Estimation
WOS KeywordRECOVERY ; PERFORMANCE ; PURSUIT ; CLUTTER ; ONLINE ; FILTER
WOS Research AreaInstruments & Instrumentation ; Optics ; Physics
WOS SubjectInstruments & Instrumentation ; Optics ; Physics, Applied
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/3854
Collection光电技术研究所被WoS收录文章
Affiliation1.Chongqing Univ, Commun Engn Coll, Chongqing 400044, Peoples R China
2.Chinese Acad Sci, Key Lab Beam Control, Chengdu 610209, Peoples R China
3.China Aerodynam Res & Dev Ctr, Mianyang 621000, Peoples R China
Recommended Citation
GB/T 7714
Li, Zhengzhou,Li, Jianing,Ge, Fengzeng,et al. Dim moving target tracking algorithm based on particle discriminative sparse representation[J]. INFRARED PHYSICS & TECHNOLOGY,2016,75:100-106.
APA Li, Zhengzhou,Li, Jianing,Ge, Fengzeng,Shao, Wanxing,Liu, Bing,&Jin, Gang.(2016).Dim moving target tracking algorithm based on particle discriminative sparse representation.INFRARED PHYSICS & TECHNOLOGY,75,100-106.
MLA Li, Zhengzhou,et al."Dim moving target tracking algorithm based on particle discriminative sparse representation".INFRARED PHYSICS & TECHNOLOGY 75(2016):100-106.
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