General structure of adaptive algorithms: adaptation and tracking
Adaptive system identification and signal processing algorithms
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Microwave Mobile Communications
Microwave Mobile Communications
Sparse LMS for system identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
RLS-weighted Lasso for adaptive estimation of sparse signals
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Comparison of SPARLS and RLS algorithms for adaptive filtering
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
Shannon-theoretic limits on noisy compressive sampling
IEEE Transactions on Information Theory
Toeplitz compressed sensing matrices with applications to sparse channel estimation
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
An adaptive greedy algorithm with application to nonlinear communications
IEEE Transactions on Signal Processing
Regularized recursive least squares for anomaly detection in sparse channel tracking applications
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Online dictionary learning algorithm with periodic updates and its application to image denoising
Expert Systems with Applications: An International Journal
Hi-index | 35.69 |
We develop a recursive L1-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an expectation-maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multipath wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely used recursive least squares (RLS) algorithm in terms. of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (With slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap-weight vectors with fixed Support.