Paper Info
Title
Variable Dimension Measurement Matrix Construction for Compressive Sampling via m Sequence
Abstract
Signal acquisition in ultra-high frequency is a challenging problem due to high cost of analog-digital converter. While compressed sensing (CS) provides an alternative way to sample signal with low sampling rate, the construction of measurement matrix is still challenging due to hardware complexity and random generation. To address this challenge, a variable dimension deterministic measurement matrix construction method is proposed in this paper based on cross-correlation characteristics of m sequences. Specifically, a lower bound of the spark of measurement matrix is derived theoretically. The proposed measurement matrix construction method is applicable to compressive sampling system to improve the quality of signal reconstruction, especially for modulated wideband converter (MWC) architecture. Simulation results demonstrate that the proposed measurement matrix is superior to random Gauss matrix and random Bernoulli matrix.
Year
DOI
Venue
2017
10.1007/978-3-319-73564-1_22
international conference on machine learning
Field
DocType
Citations 
Wideband,Spark (mathematics),Pattern recognition,Matrix (mathematics),Upper and lower bounds,Computer science,Sampling (signal processing),Algorithm,Artificial intelligence,Compressed sensing,Signal reconstruction,Bernoulli's principle
Conference
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Jingting Xiao100.34
Ruoyu Zhang262.79
Honglin Zhao364.82