Geometric constraints in sensing matrix design for compressed sensing

2020 ◽  
Vol 171 ◽  
pp. 107498
Author(s):  
C.H. Pimentel-Romero ◽  
M. Mangia ◽  
F. Pareschi ◽  
R. Rovatti ◽  
G. Setti
Keyword(s):  
Compressed Sensing ◽  
Geometric Constraints ◽  
Sensing Matrix ◽  
Matrix Design

A novel multi-dictionary framework with global sensing matrix design for compressed sensing

2018 ◽  
Vol 152 ◽  
pp. 69-78 ◽  
Author(s):  
Jiajun Ding ◽  
Donghai Bao ◽  
Qingpei Wang ◽  
Xiongxiong He ◽  
Huang Bai ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Sensing Matrix ◽  
Matrix Design

scholarly journals A Method of Reweighting the Sensing Matrix for Compressed Sensing

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21425-21432
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Qian Wang
Keyword(s):  
Compressed Sensing ◽  
Sensing Matrix

Research on Compressed Sensing Matrix Based on Data Compressing of Insulator Leakage Current

2013 ◽  
Vol 475-476 ◽  
pp. 451-454
Author(s):  
Xue Ming Zhai ◽  
Xiao Bo You ◽  
Ruo Chen Li ◽  
Yu Jia Zhai ◽  
De Wen Wang
Keyword(s):  
Compressed Sensing ◽  
Data Compression ◽  
Leakage Current ◽  
Sparse Matrix ◽  
Circulant Matrix ◽  
Measurement Matrix ◽  
Sensing Matrix ◽  
Significant Step ◽  
On Line

Insulator fault may lead to the accident of power network,thus the on-line monitoring of insulator is very significant. Low rates wireless network is used for data transmission of leakage current. Making data compression and reconstruction of leakage current with the compressed sensing theory can achieve pretty good results. Determination of measurement matrix is the significant step for realizing the compressed sensing theory. This paper compares multiple measurement matrix of their effect via experiments, putting forward to make data compression and reconstruction of leakage current using Toeplitz matrix, circulant matrix and sparse matrix as measurement matrix, of which the reconstitution effect is almost the same as classical measurement matrix and depletes computational complexity and workload.

scholarly journals A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
XiangHua Yao ◽  
Feng Lian
Keyword(s):  
Compressed Sensing ◽  
Phased Array ◽  
Reduction Method ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Sensing Matrix ◽  
Replacement Method ◽  
Precise Estimation ◽  
Novel Method ◽  
Measurement Vector

A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.

Expander Graph-Based Structured Random Sensing Matrix in Compressed Sensing

2021 ◽  
pp. 629-637
Author(s):  
Sujit Das ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Compressed Sensing ◽  
Expander Graph ◽  
Sensing Matrix
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