Open AccessBacktracking based Joint-Sparse Signal Recovery for Distributed Compressive Sensing
Author(s) -
Srinidhi Murali,
Sheshadri Narayanan,
Dheeraj Prasanna,
Jani Anbarasi L
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a4778.129219
Subject(s) - compressed sensing , computer science , joint (building) , backtracking , signal (programming language) , signal recovery , signal reconstruction , algorithm , a priori and a posteriori , dual (grammatical number) , computational complexity theory , scheme (mathematics) , pattern recognition (psychology) , signal processing , artificial intelligence , mathematics , digital signal processing , architectural engineering , art , mathematical analysis , philosophy , literature , epistemology , computer hardware , engineering , programming language
In Distributed Compressive Sensing (DCS), the Joint Sparsity Model (JSM) refers to an ensemble of signals being jointly sparse. In [4], 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.