Name
Abstract | ||
|---|---|---|
In this letter, we propose a compressive sensing approach for synthetic aperture radar (SAR) imaging of sparse scenes with 1-bit-quantized data. Within the framework of maximum a posteriori estimation, we formulate the SAR image reconstruction problem as a sparse optimization problem and then solve it using a first-order primal-dual algorithm. The processing results of both simulated and real radar data show that our approach can eliminate the ghost target caused by 1-bit quantization in high signal-to-noise ratio situations and suppress the noisy background very well. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LGRS.2015.2390623 | IEEE Geosci. Remote Sensing Lett. |
Keywords | Field | DocType |
optimisation,synthetic aperture radar,1-bit quantization,image coding,maximum a posteriori (map),sparse optimization problem,radar data processing,map approach,synthetic aperture radar (sar),1-bit compressive sensing,maximum likelihood estimation,sparse scenes,quantisation (signal),maximum aposteriori estimation,synthetic aperture radar imaging,sar image reconstruction problem,image reconstruction,1-bit compressive sensing (cs),first-order primal dual algorithm,compressed sensing,natural scenes,interference suppression,sparsity,noisy background suppression,radar imaging,signal-to-noise ratio,signal to noise ratio,imaging | Iterative reconstruction,Pulse-Doppler radar,Radar engineering details,Continuous-wave radar,Radar,Computer vision,Radar imaging,Synthetic aperture radar,Remote sensing,Inverse synthetic aperture radar,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 6 | 1545-598X |
Citations | PageRank | References |
4 | 0.40 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Dong | 1 | 4 | 1.08 |
| Yunhua Zhang | 2 | 14 | 5.77 |