EM Based p-norm-like Constraint RLS Algorithm for Sparse System Identification

Author:

Shuyang Jiang, Kung Yao

Keyword:

Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)

journal:

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date:

2023-12-10 00:00:00

Abstract

In this paper, the recursive least squares (RLS) algorithm is considered in the sparse system identification setting. The cost function of RLS algorithm is regularized by a $p$-norm-like ($0 \leq p \leq 1$) constraint of the estimated system parameters. In order to minimize the regularized cost function, we transform it into a penalized maximum likelihood (ML) problem, which is solved by the expectation-maximization (EM) algorithm. With the introduction of a thresholding operator, the update equation of the tap-weight vector is derived. We also exploit the underlying sparsity to implement the proposed algorithm in a low computational complexity fashion. Numerical simulations demonstrate the superiority of the new algorithm over conventional sparse RLS algorithms, as well as regular RLS algorithm.