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
Line detection in images through regularized hough transform
IEEE Transactions on Image Processing
Compressive sensing of underground structures using GPR
Digital Signal Processing
Hi-index | 35.68 |
A novel data acquisition and imaging method is presented for stepped-frequency continuous-wave ground penetrating radars (SFCW GPRs). It is shown that if the target space is sparse, i.e., a small number of point like targets, it is enough to make measurements at only a small number of random frequencies to construct an image of the target space by solving a convex optimization problem which enforces sparsity through l1 minimization. This measurement strategy greatly reduces the data acquisition time at the expense of higher computational costs. Imaging results for both simulated and experimental GPR data exhibit less clutter than the standard migration methods and are robust to noise and random spatial sampling. The images also have increased resolution where closely spaced targets that cannot be resolved by the standard migration methods can be resolved by the proposed method.