Backtracking based Joint-Sparse Signal Recovery for Distributed Compressive Sensing
Srinidhi Murali1, Sathiya Narayanan2, Prasanna D R L3, Jani Anbarasi L4
1Srinidhi Murali*, School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Sathiya Narayanan, School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai (Tamil Nadu), India.
3Prasanna D R L, Department of Information Technology, Vasavi College of Engineering, Hyderabad (Telangana), India.
4Jani Anbarasi L, School of Computing Science and Engineering (SCSE), Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 3919-3922 | Volume-9 Issue-2, December 2019. | Retrieval Number: A4778119119/2019©BEIESP | DOI: 10.35940/ijitee.A4778.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Distributed Compressive Sensing (DCS), the Joint Sparsity Model (JSM) refers to an ensemble of signals being jointly sparse. In , a joint reconstruction scheme was proposed using a single linear program. However, for reconstruction of any individual sparse signal using that scheme, the computational complexity is high. In this paper, we propose a dual-sparse signal reconstruction method. In the proposed method, if one signal is known apriori, then any other signal in the ensemble can be efficiently estimated using the proposed method, exploiting the dual-sparsity. Simulation results show that the proposed method provides fast and efficient recovery.
Keywords: Compressive Sensing, Sparse Reconstruction, and Distributed Compressive Sensing.
Scope of the Article: Signal Control System & Processing