Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Direction-of-arrival estimation using a mixed l2,0norm approximation
IEEE Transactions on Signal Processing
An affine scaling methodology for best basis selection
IEEE Transactions on Signal Processing
Subset selection in noise based on diversity measure minimization
IEEE Transactions on Signal Processing
Sparse Representation in Structured Dictionaries With Application to Synthetic Aperture Radar
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
Hi-index | 0.08 |
the compressive sensing (CS) based ISAR imaging has exhibited high-resolution imaging quality when faced with limited spatial aperture. However, its performance is significantly dependent on the number of pulses and the noise level. In this paper, from the perspective of promoted sparsity constraint, a novel reconstruction model deducted from Meridian prior (MCS) is proposed. The detailed comparison of the proposed MCS model with the Laplace-prior-based CS model (LCS) is conducted. The Lorentz curve analysis testified the enhanced sparsity of the MCS model. Different from the algorithm for LCS model, in our solution procedure, the variance parameter is iteratively updated until the algorithm converges. Simulations and the ground truth data experiments of ISAR show that, with the decrease of the number of pulses and signal-to-noise ratio, the proposed model exhibits better performance in terms of resolution and amplitude error than that of the LCS model.