High-Resolution Radar Imaging in Low SNR Environments Based on Expectation Propagation

We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering ce...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-02, Vol.59 (2), p.1275-1284
Main Authors: Bai, Xueru, Wang, Ge, Liu, Siqi, Zhou, Feng
Format: Article
Language:English
Subjects:
Azimuth
Bayes methods
Bayesian analysis
Bayesian inference
expectation propagation (EP)
High resolution
image focusing
Image resolution
Imaging
Imaging techniques
Mathematical models
Maximum likelihood estimation
Numerical methods
Optimization
Parameter estimation
Probabilistic inference
Probability theory
Propagation
Radar
Radar imaging
Resolution
Scattering
Signal to noise ratio
Statistical inference
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Description
Summary:We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering centers. Then, the model parameters and phase errors are estimated iteratively by expectation propagation (EP) and maximum likelihood (ML) estimation. Compared with the available imaging methods based on the numerical optimization and Bayesian inference, the proposed method has exhibited more flexibility in data representation and better performance in parameter estimation, particularly in sparse-aperture and low SNR scenarios.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3004006