JACIII Vol.20 No.7 pp. 1119-1126
doi: 10.20965/jaciii.2016.p1119


Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms

Chen Ye*, Guan Gui**, Shin-ya Matsushita*, and Li Xu*

*Department of Electronics and Information Systems, Akita Prefectural University
84-4 Ebinokuchi, Tsuchiya Aza, Yurihonjo, Akita 015-0055, Japan

**College of Telecommunication and Information Engineering, Nanjing University of Post and Telecommunications
No. 66, New Mofan Rd., Gulou District, Nanjing 210003, China

July 6, 2016
September 29, 2016
Online released:
December 20, 2016
December 20, 2016
compressive sensing, sparse signal reconstruction, block-structured sparsity, least mean square, sparse constraint

Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “block 0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.

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Last updated on Mar. 28, 2017