Novel ISAR autofocusing method based on Bayesian inference

To achieve well-focused ISAR imaging with low signal-to-noise ratio (SNR), this study constructs a statistical model and derives an effective method for image autofocusing based on Bayesian inference. Particularly, hierarchical sparse-promoting priors are imposed on the weights, which are conjugate...

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Bibliographic Details
Published inJournal of engineering (Stevenage, England) Vol. 2019; no. 19; pp. 5793 - 5796
Main Authors Bai, Xueru, Wang, Ge
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 01.10.2019
Wiley
Subjects
Bayes methods
Bayesian inference
closed-form solution
hierarchical sparse-promoting priors
IET International Radar Conference (IRC 2018)
image autofocusing
inference mechanisms
ISAR autofocusing method
ISAR imaging
radar computing
radar imaging
signal-to-noise ratio
SNR
statistical analysis
statistical model
synthetic aperture radar
ISAR autofocusing method
hierarchical sparse-promoting priors
ISAR imaging
statistical model
inference mechanisms
closed-form solution
synthetic aperture radar
radar computing
SNR
signal-to-noise ratio
radar imaging
Bayes methods
statistical analysis
Bayesian inference
image autofocusing
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Summary:To achieve well-focused ISAR imaging with low signal-to-noise ratio (SNR), this study constructs a statistical model and derives an effective method for image autofocusing based on Bayesian inference. Particularly, hierarchical sparse-promoting priors are imposed on the weights, which are conjugate to the likelihood. The method has closed-form solution which facilitates computation. Experimental results have demonstrated the effectiveness of the method in low SNR and short aperture scenarios.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2019.0342