Title
A novel sparse system estimation method based on least squares, ℓ1-norm minimization and shrinkage
Abstract
In this article, a novel low-complexity block-processing sparse system estimation method, based on least squares (LS), ℓ1-norm minimization and support shrinkage, is proposed. The proposed method can be seen as a counterpart for the Least Absolute Shrinkage and Selection Operator (LASSO), in the sense that the proposed method aims to find the vector that minimizes its ℓ1-norm subject to a maximum arbitrary value Jmax for the LS cost function. Thus, it is suitable to be used when there is no a priori knowledge of the maximum ℓ1-norm value of the system impulse response. In addition, making Jmax directly proportional to the minimum LS cost function grants the proposed method low sensitivity to wide ranges of signal to noise-plus-interference ratio. Simulation results show that the proposed method has better convergence performance than the ordinary Full-support Least Squares (LS), the Recursive Least Squares with ℓ1-norm regularization (ℓ1-RLS), the Relaxations and the Basis Pursuit Denoising (BPDN) estimation methods.
Year
DOI
Venue
2015
10.1109/ICCNC.2015.7069465
Computing, Networking and Communications
Keywords
DocType
Citations 
compressed sensing,least mean squares methods,minimisation,recursive estimation,signal denoising,transient response,ℓ1-norm minimization,ℓ1-norm regularisation,BPDN estimation method,LASSO,LS cost function,basis pursuit denoising,least absolute shrinkage and selection operator,low complexity block processing sparse system estimation method,recursive least square,relaxations estimation method,signal to noise-plus-interference ratio,system impulse response,ℓ1-norm minimization,Sparse system identification,block-processing,compressive sensing,convex optimization,least squares,shrinkage,sparse channel estimation,ultra-wideband
Conference
0
PageRank 
References 
Authors
0.34
0
6