Model-based processing in sensor arrays
Advances in spectrum analysis and array processing (vol. III)
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution
Signal Processing - Sparse approximations in signal and image processing
Linear programming in spectral estimation. Application to array processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
On sparse representation in pairs of bases
IEEE Transactions on Information Theory
On sparse representations in arbitrary redundant bases
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Error bounds for convex parameter estimation
Signal Processing
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The problem of multiple emitters direction finding using an array of sensors is addressed. We describe a sparsity-based covariance-matrix fitting method. The procedure consists of finding a sparse representation of the sample covariance matrix, using an over-complete basis obtained from array manifold samples. Sparsity is encouraged by an l1-norm penalty function. The penalty function is minimized efficiently by linear programming. The proposed method is simple enough to provide useful insight and it does not require the identification of the signal and noise subspaces. Therefore, the method does not rely on a good estimate of the number of emitters. Some of the approach properties are super-resolution, robustness to noise, robustness to emitter correlation, and no sensitivity to initialization. Special emphasis is given to uncorrelated sources and uniform linear arrays.