This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications which work with multiple signals maintaining some degree of dependency such as radar and sensor networks. In this paper, we introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion on the convergence of our method called Simultaneous Iterative Method (SIM). Additionally, we compare our method with other group-sparse reconstruction techniques, i.e., Simultaneous Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive Thresholding (BIMAT) through numerical experiments. The simulation results demonstrate that SIM outperforms these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIM is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems.
Sadrizadeh, S., Kiani, S., Boloursaz, M., Marvasti, F. (2019). Iterative method for simultaneous sparse approximation. Scientia Iranica, 26(3), 1601-1607. doi: 10.24200/sci.2018.5564.1347
MLA
Seyedeh Sahar Sadrizadeh; Shahrzad Kiani; Mehdi Boloursaz; Farokh Marvasti. "Iterative method for simultaneous sparse approximation". Scientia Iranica, 26, 3, 2019, 1601-1607. doi: 10.24200/sci.2018.5564.1347
HARVARD
Sadrizadeh, S., Kiani, S., Boloursaz, M., Marvasti, F. (2019). 'Iterative method for simultaneous sparse approximation', Scientia Iranica, 26(3), pp. 1601-1607. doi: 10.24200/sci.2018.5564.1347
VANCOUVER
Sadrizadeh, S., Kiani, S., Boloursaz, M., Marvasti, F. Iterative method for simultaneous sparse approximation. Scientia Iranica, 2019; 26(3): 1601-1607. doi: 10.24200/sci.2018.5564.1347
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