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
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 ℓ0-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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