scholarly journals Retrieval of sparse solutions of multiple-measurement vectors via zero-point attracting projection

2012 ◽  
Vol 92 (12) ◽  
pp. 3075-3079 ◽  
Author(s):  
Yang You ◽  
Laming Chen ◽  
Yuantao Gu ◽  
Wei Feng ◽  
Hui Dai
Keyword(s):  
Zero Point ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Sparse Solutions

Sparse solutions to linear inverse problems with multiple measurement vectors

2005 ◽  
Vol 53 (7) ◽  
pp. 2477-2488 ◽  
Author(s):  
S.F. Cotter ◽  
B.D. Rao ◽  
Kjersti Engan ◽  
K. Kreutz-Delgado
Keyword(s):  
Inverse Problems ◽  
Multiple Measurement ◽  
Linear Inverse Problems ◽  
Multiple Measurement Vectors ◽  
Sparse Solutions

scholarly journals Off-the-grid recovery of time and frequency shifts with multiple measurement vectors

2021 ◽  
Vol 183 ◽  
pp. 108016
Author(s):  
Maral Safari ◽  
Sajad Daei ◽  
Farzan Haddadi
Keyword(s):  
Frequency Shifts ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

scholarly journals Fusion of sparse reconstruction algorithms for multiple measurement vectors

Sadhana ◽  
2016 ◽  
Vol 41 (11) ◽  
pp. 1275-1287
Author(s):  
K G Deepa ◽  
Sooraj K Ambat ◽  
K V S Hari
Keyword(s):  
Sparse Reconstruction ◽  
Reconstruction Algorithms ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

scholarly journals Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Rui Li ◽  
Ying Luo ◽  
Qun Zhang ◽  
Yijun Chen ◽  
Jia Liang
Keyword(s):  
Bayesian Learning ◽  
Radar Imaging ◽  
Bistatic Radar ◽  
Target Feature ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Feature Information ◽  
Coincidence Imaging ◽  
Rotating Cone ◽  
Imaging Performance

Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.

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