On compressive sensing applied to radar

By Joachim H. G. Ender. Signal Processing, 2010

684 citations recorded by the source corpus.

Subfields: Applied Mathematics, Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/19a765322a4658d2a85151f8a97b808ea518c531

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Bhaskar D. Rao
2. Richard Bamler
3. Zhilin Zhang
4. Xiaoxiang Zhu

In Biomedical Engineering

1. Xiaoxiang Zhu
2. Richard Bamler
3. Bhaskar D. Rao
4. Zhilin Zhang

In Computational Mechanics

1. Zhilin Zhang
2. Bhaskar D. Rao
3. Richard Bamler
4. Xiaoxiang Zhu
5. Giacomo Oliveri
6. Andrea Massa
7. Paolo Rocca
8. David Paul Wipf

Citations in the corpus

• Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning (2011, background): “(SMV) model (1) has a wide range of applications, such as electroencephalography (EEG)/Magnetoencephalography (MEG) source localization [7], direction-of-arrival (DOA) estimation [8], radar detection [9], and magnetic resonance imaging (MRI) [10]. Motivated by many applications such as EEG/MEG sourc”
• Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR (2012, methodology): “Their projections onto the elevation direction have a size of about 1/30 of the elevation resolution and, hence, can be treated as a few individual delta functions (typically 1–4) [10].”