Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Range-Doppler imaging via a train of probing pulses
IEEE Transactions on Signal Processing
An adaptive filtering approach to spectral estimation and SARimaging
IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
IEEE Transactions on Signal Processing
Rank-deficient robust Capon filter bank approach to complex spectral estimation
IEEE Transactions on Signal Processing - Part I
Sparse Representation in Structured Dictionaries With Application to Synthetic Aperture Radar
IEEE Transactions on Signal Processing - Part I
SAR imaging via modern 2-D spectral estimation methods
IEEE Transactions on Image Processing
Using APES for interferometric SAR imaging
IEEE Transactions on Image Processing
Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization
IEEE Transactions on Image Processing
IAA Spectral Estimation: Fast Implementation Using the Gohberg–Semencul Factorization
IEEE Transactions on Signal Processing
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We introduce a new approach using the Bayesian framework for the reconstruction of sparse Synthetic Aperture Radar (SAR) images. The algorithm, named SLIM, can be thought of as a sparse signal recovery algorithm with excellent sidelobe suppression and high resolution properties. For a given sparsity promoting prior, SLIM cyclically minimizes a regularized least square cost function. We show how SLIM can be used for SAR image reconstruction as well as SAR image enhancement. We evaluate the performance of SLIM by using realistically simulated complex-valued backscattered data from a backhoe vehicle. The numerical results show that SLIM can satisfactorily suppress the sidelobes and yield higher resolution than the conventional matched filter or delay-and-sum (DAS) approach. SLIM outperforms the widely used compressive sampling matching pursuit (CoSaMP) algorithm, which requires the delicate choice of user parameters. Compared with the recently developed iterative adaptive approach (IAA), which iteratively solves a weighted least squares problem, SLIM is much faster. Due to the computational complexity involved with SAR imaging, we show how SLIM can be made even more computationally efficient by utilizing the fast Fourier transform (FFT) and conjugate gradient (CG) method to carry out its computations. Furthermore, since SLIM is derived under the Bayesian model, the a posteriori distribution given by the algorithm provides us with a confident measure regarding the statistical properties of the SAR image pixels.