NDLI: Dual Branch Compressive Sensing for analog signal

Content Provider	IEEE Xplore Digital Library
Author	Hua Ai Wenbin Guo Yang Lu Xing Wang Wenbo Wang
Copyright Year	2011
Description	Author affiliation: Wireless Signal Processing and Network Lab, Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts & Telecommunications (BUPT), 100876, China (Hua Ai; Wenbin Guo; Yang Lu; Xing Wang; Wenbo Wang)
Abstract	The framework of Analog-to-Information Converter (AIC) enables the feasibility of applying Compressive Sensing (CS) theory into practice. Based on the AIC structure, sparse signals are compressed at sub-Nyquist sampling rate, which dramatically reduces the hardware pressure of A/D chip. However, not fully exploiting the inner correlation of complex frequency, AIC structure does not work well in non-ideal environment or some strict conditions. In this paper, we propose a Dual Branch Compressive Sensing (DBCS) framework in light of the symmetry of Discrete Fourier Transform (DFT). By recovering the real and imaginary parts of complex-valued frequency respectively, we find a way to remove redundancy in reconstructing a complex frequency. Numerical experiments have shown the DBCS structure improves the performance of anti-noise and compressibility remarkably.
Starting Page	801
Ending Page	805
File Size	174178
Page Count	5
File Format	PDF
ISBN	9781612843063
e-ISBN	9781612843070
DOI	10.1109/ICCT.2011.6157988
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Correlation Matching pursuit algorithms Mathematical model Compressed sensing Equations Reconstruction algorithms dual branch Compressive sensing inner correlation AIC
Content Type	Text
Resource Type	Article

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