Superresolution via sparsity constraints
SIAM Journal on Mathematical Analysis
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Iterative space-time domain fast multiresolution sar imaging algorithms
Iterative space-time domain fast multiresolution sar imaging algorithms
Convex Optimization
Sparse Signal Reconstruction from Noisy Compressive Measurements using Cross Validation
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Compressed Sensing and Redundant Dictionaries
IEEE Transactions on Information Theory
Two-dimensional random projection
Signal Processing
Compressive sensing of underground structures using GPR
Digital Signal Processing
Artificial Life and Robotics
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The theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of non-adaptive linear measurements by solving a convex @?"1 minimization problem. This paper presents a novel data acquisition system for wideband synthetic aperture imaging based on CS by exploiting sparseness of point-like targets in the image space. Instead of measuring sensor returns by sampling at the Nyquist rate, linear projections of the returned signals with random vectors are used as measurements. Furthermore, random sampling along the synthetic aperture scan points can be incorporated into the data acquisition scheme. The required number of CS measurements can be an order of magnitude less than uniform sampling of the space-time data. For the application of underground imaging with ground penetrating radars (GPR), typical images contain only a few targets. Thus we show, using simulated and experimental GPR data, that sparser target space images are obtained which are also less cluttered when compared to standard imaging results.