Name
Abstract | ||
|---|---|---|
Sparse estimation and compressive sensing techniques have been recently considered for radar estimation problems. It is frequently observed that these methods are robust to model uncertainties and substantially improve performance in scenarios with a low signal-to-noise. However, since current sparsity-based techniques are computationally costly and require a suitable discretization (grid), which strongly restricts resolution, they practically receive less attention. In this work, we present an application of a new sparsity-based technique to the specific problem of range-Doppler estimation. The method, generalizing basis pursuit, is less computationally complex and its performance is independent of the grid selection. We demonstrate that the proposed technique can improve estimation performance in difficult cases, as compared to the SAGE technique. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SAM.2014.6882421 | Sensor Array and Multichannel Signal Processing Workshop |
Keywords | Field | DocType |
compressed sensing,radar signal processing,compressive sensing technique,computationally complex,estimation performance improvement,generalizing basis pursuit,grid selection,radar estimation problem,range-Doppler estimation problem,signal-to-noise,sparse estimation,sparsity-based technique,suitable discretization | Radar,Discretization,Mathematical optimization,Generalization,Computer science,Continuum (design consultancy),Basis pursuit,Doppler effect,Statistics,Compressed sensing,Grid | Conference |
ISSN | Citations | PageRank |
1551-2282 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashkan Panahi | 1 | 0 | 0.34 |
| Marie Ström | 2 | 0 | 0.34 |
| Mats Viberg | 3 | 1043 | 126.67 |