Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Sparse LMS for system identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
SPARLS: the sparse RLS algorithm
IEEE Transactions on Signal Processing
Online adaptive estimation of sparse signals: where RLS meets the l1-norm
IEEE Transactions on Signal Processing
Adaptive algorithms for sparse system identification
Signal Processing
Low-Complexity RLS Algorithms Using Dichotomous Coordinate Descent Iterations
IEEE Transactions on Signal Processing - Part II
Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted Balls
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
A Family of Robust Algorithms Exploiting Sparsity in Adaptive Filters
IEEE Transactions on Audio, Speech, and Language Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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We present a greedy recursive algorithm for computing sparse solutions to systems of linear equations. Derived from adaptive matching pursuit, the algorithm employs a greedy column selection strategy which, combined with coefficient update via coordinate descent, ensures a low complexity. The sparsity level is estimated online using the predictive least squares (PLS) criterion. The key to performance is the minimization of two residuals, corresponding to two solutions with different sparsity levels, one for finding the values of the nonzero coefficients, the other for maintaining a large enough pool of candidates for the PLS criterion. We test the algorithm for a sparse time-varying finite impulse response channel; the performance is comparable with or better than that of the competing methods, while the complexity is lower.