Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Complex sequences with low periodic correlations (Corresp.)
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressed sensing of complex-valued data
Signal Processing
Realistic simulations of aorta radius estimation
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Sparse Doppler-only snapshot imaging for space debris
Signal Processing
Compressed Sensing via Dimension Spread in Dimension-Restricted Systems
Wireless Personal Communications: An International Journal
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Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signals with a reduced number of samples. Today, modern radar systems operate with high bandwidths-demanding high sample rates according to the Shannon-Nyquist theorem-and a huge number of single elements for phased array antennas. Often only a small amount of target parameters is the final output, arising the question, if CS could not be a good mean to reduce data size, complexity, weight, power consumption and costs of radar systems. There is only a small number of publications addressing the application of CS to radar, leaving several open questions. This paper addresses some aspects as a further step to CS-radar by presenting generic system architectures and implementation considerations. It is not the aim of this paper to investigate numerically efficient algorithms but to point to promising applications as well as arising problems. Three possible applications are considered: pulse compression, radar imaging, and air space surveillance with array antennas. Some simulation results are presented and enriched by the evaluation of real data acquired by an experimental radar system of Fraunhofer FHR.