Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning
Joint Authors
Qin, Yu-Liang
Zhou, Xiaoli
Cheng, Yongqiang
Wang, Hongqiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-08
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Radar coincidence imaging (RCI) is a high-resolution staring imaging technique motivated by classical optical coincidence imaging.
In RCI, sparse reconstruction methods are commonly used to achieve better imaging result, while the performance guarantee is based on the general assumption that the scatterers are located at the prediscretized grid-cell centers.
However, the widely existing off-grid problem degrades the RCI performance considerably.
In this paper, an algorithm based on variational sparse Bayesian learning (VSBL) is developed to solve the off-grid RCI.
Applying Taylor expansion, the unknown true dictionary is approximated accurately to a linear model.
Then target reconstruction is reformulated as a joint sparse recovery problem that recovers three groups of sparse coefficients over three known dictionaries with the constraint of the common support shared by the groups.
VSBL is then applied to solve the problem by assigning appropriate priors to the three groups of coefficients.
Results of numerical experiments demonstrate that the algorithm can achieve outstanding reconstruction performance and yield superior performance both in suppressing noise and in adapting to off-grid error.
American Psychological Association (APA)
Zhou, Xiaoli& Wang, Hongqiang& Cheng, Yongqiang& Qin, Yu-Liang. 2016. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
Modern Language Association (MLA)
Zhou, Xiaoli…[et al.]. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
American Medical Association (AMA)
Zhou, Xiaoli& Wang, Hongqiang& Cheng, Yongqiang& Qin, Yu-Liang. Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111794
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111794