Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
Foundations of Computational Mathematics
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
On compressive sensing applied to radar
Signal Processing
IEEE Transactions on Signal Processing
Image compressed sensing based on wavelet transform in contourlet domain
Signal Processing
Robust ISAR imaging based on compressive sensing from noisy measurements
Signal Processing
Optimized Projections for Compressed Sensing
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
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
Deterministic Construction of Binary, Bipolar, and Ternary Compressed Sensing Matrices
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Structured Compressed Sensing: From Theory to Applications
IEEE Transactions on Signal Processing
Measurement Matrix Design for Compressive Sensing–Based MIMO Radar
IEEE Transactions on Signal Processing
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Deterministic sensing matrices are useful, because in practice, the sampler has to be a deterministic matrix. It is quite challenging to design a deterministic sensing matrix with low coherence. In this paper, we consider a more general condition, when the deterministic sensing matrix has high coherence and does not satisfy the restricted isometry property (RIP). A novel algorithm, called the similar sensing matrix pursuit (SSMP), is proposed to reconstruct a K-sparse signal, based on the original deterministic sensing matrix. The proposed algorithm consists of off-line and online processing. The goal of the off-line processing is to construct a similar compact sensing matrix containing as much information as possible from the original sensing matrix. The similar compact sensing matrix has low coherence, which guarantees a perfect reconstruction of the sparse vector with high probability. The online processing begins when measurements arrive, and consists of rough and refined estimation processes. Results from our simulation show that the proposed algorithm obtains much better performance while coping with a deterministic sensing matrix with high coherence compared with the subspace pursuit (SP) and basis pursuit (BP) algorithms.