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 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

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.

Signal recovery from multiple measurement vectors via tunable random projection and boost

2012 ◽  
Vol 92 (12) ◽  
pp. 2901-2908 ◽  
Author(s):  
Jianxin Gai ◽  
Ping Fu ◽  
Zhen Li ◽  
Jiaqing Qiao
Keyword(s):  
Random Projection ◽  
Signal Recovery ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors
Sign in / Sign up

Export Citation Format

Share Document