2023/12/10 by Shuyang Jiang, Jiang, Shuyang, Kung Yao +1
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #Structural Health Monitoring Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2312.05829
openalex publication_date 2023/12/10 · openalex created_date 2023/12/13 · openalex updated_date 2026/07/28
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 ≤ p ≤ 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.