A mixed norm constraint IPNLMS algorithm for sparse channel estimation

• Published in: Signal, image and video processing image and video processing, 2022-03, Vol.16 (2), p.457-464
• Main Authors: Wu, Fei-Yun, Song, Yan-Chong, Tian, Tian, Yang, Kunde, Duan, Rui, Sheng, Xueli
• Format: Article
• Language: English
• Subjects: Algorithms; Clusters; Computer Imaging; Computer Science; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Regularization; Signal,Image and Speech Processing; System effectiveness; System identification; Vision
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-021-01975-6

This paper presents a novel approach for structure extraction of the cluster sparse system identification. Different from adopting ℓ 1 -norm constraint to regularize the sparsity in the improved proportionate normalized least mean square (IPNLMS) algorithm, we directly work with the block sparse structure via ℓ 1 , 0 -norm constraint. In particular, we develop a cluster sparse IPNLMS by the block ℓ 0 norm regularization, named IPNLMS-BL0 method. The cluster sparse constraint is regarded as an extended version for the sparse constraint term. On the other hand, the iterations of IPNLMS-BL0 are derived by the steepest descent strategy. Then, we provide the analysis of block size choices of the cluster sparse constraint, computational complexity, and steady-state error of the proposed method. Various simulations are designed to test the performance of the IPNLMS-BL0 algorithm and its counterparts to identify and track the unknown sparse systems. The results are provided and analyzed to confirm the effectiveness and superiority of the proposed IPNLMS-BL0 algorithm.