Compressive Sensing Applied to MIMO Radar and Sparse Disjoint Scenes

2021 ◽  
Author(s):  
Michael Francis Minner
Author(s):  
Nafiseh Shahbazi ◽  
Aliazam Abbasfar ◽  
Mohammad Jabbarian-Jahromi

2014 ◽  
Vol 50 (2) ◽  
pp. 898-909 ◽  
Author(s):  
Yao Yu ◽  
Shunqiao Sun ◽  
Rabinder N. Madan ◽  
Athina Petropulu

2019 ◽  
Vol 55 (1) ◽  
pp. 318-331 ◽  
Author(s):  
Soheil Salari ◽  
Francois Chan ◽  
Yiu-Tong Chan ◽  
Il-Min Kim ◽  
Roger Cormier

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Tao ◽  
Gong Zhang ◽  
Jindong Zhang

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.


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