In this paper we propose a new algorithm for solving the blind source separation (BSS) problem when the source signals are known to be sparse, or can be sparsely represented in some dictionary.
•
The algorithm capitalizes on a previous result that shows certain classes of nonconvex functions perform better than the convex l1-norm in measuring sparsity of a signal.
•
In this paper we propose a majorization–minimization (MM) method for minimizing such a nonconvex objective function. The MM technique is based on locally replacing the original nonconvex function by a smooth convex function that can be efficiently minimized.
•
We proof that the global minimum of the suggested surrogate function is guaranteed to reduce the value of the original nonconvex function.
•
Following the proposed technique, the sparse BSS problem is reduced to an iterative computation of the minor eigenvectors of particular covariance matrices. These features permit a computationally efficient implementation.
•
The proposed algorithm enjoys several advantages such as robustness to noise and the ability to estimate the number of source signals.
•
Numerical results show that the proposed algorithm outperforms other well-known algorithms that solve the same problem.
NGLC 2004-2010.National Geological Library of China All Rights Reserved.
Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
For exchange or info please contact us via email.