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Off-Grid Radar Coincidence Imaging Based on Variational Sparse Bayesian Learning
- Source :
- Mathematical Problems in Engineering, Vol 2016 (2016)
- Publication Year :
- 2016
- Publisher :
- Hindawi Limited, 2016.
-
Abstract
- 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.
- Subjects :
- Mathematical optimization
Article Subject
General Mathematics
02 engineering and technology
Bayesian inference
Coincidence
law.invention
symbols.namesake
law
Prior probability
0202 electrical engineering, electronic engineering, information engineering
Taylor series
Radar
Mathematics
lcsh:Mathematics
General Engineering
Linear model
020206 networking & telecommunications
lcsh:QA1-939
Grid
lcsh:TA1-2040
symbols
020201 artificial intelligence & image processing
Noise (video)
lcsh:Engineering (General). Civil engineering (General)
Algorithm
Subjects
Details
- ISSN :
- 15635147 and 1024123X
- Volume :
- 2016
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....549addb56bcd2f59716b7a1af88cc497