Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Radar Array Processing
Dictionary learning algorithms for sparse representation
Neural Computation
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Space Time Coding for Broadband Wireless Communications
Space Time Coding for Broadband Wireless Communications
EURASIP Journal on Applied Signal Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
IEEE Transactions on Signal Processing - Part II
Extended derivations of MUSIC in the presence of steering vector errors
IEEE Transactions on Signal Processing
Doubly constrained robust Capon beamformer
IEEE Transactions on Signal Processing
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
Sparse Channel Estimation with Zero Tap Detection
IEEE Transactions on Wireless Communications
A generalized uncertainty principle and sparse representation in pairs of bases
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
Sparse signal reconstruction using decomposition algorithm
Knowledge-Based Systems
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Array processing algorithms are used in many applications for source localization and signal waveform estimation. When the number of snapshots is small and/or the signal-to-noise ratio (SNR) is low, it becomes a challenge to discriminate closely-spaced sources. In this paper, two new array processing algorithms exploiting sparsity are proposed to overcome this problem. The first proposed method combines a well-known sparsity preserving algorithm, namely the least absolute shrinkage and selection operator (LASSO), with the Bayesian information criterion (BIC) to eliminate user parameters. The second proposed algorithm extends the sphere decoding algorithm, which is widely used in communication applications for the recovery of signals belonging to a finite integer dictionary, to promote the sparsity of the solution. The proposed algorithms are compared with several existing sparse signal estimation techniques. Simulations involving uncorrelated and coherent sources demonstrate that the proposed algorithms, especially the algorithm based on sphere decoding, show better performance than the existing methods. Moreover, the proposed algorithms are shown to be more practical than the existing methods due to the easiness in selecting their user parameters.