SAR Ground Moving Target Imaging Algorithm Based on Parametric and Dynamic Sparse Bayesian Learning
In this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time-frequency representation, which is known as Lv's distribution (LVD), is employed on the moving t...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/86047 http://hdl.handle.net/10220/43922 |