Low Complexity Cyclic Feature Recovery Based on Compressed Sampling

To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing...

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Bibliographic Details
Published inInternational journal of distributed sensor networks Vol. 2015; no. 10; pp. 1 - 7
Main Authors Sun, Zhuo, Hou, Jia, Liu, Siyuan, Wang, Sese, Chen, Xuantong
Format Journal Article
LanguageEnglish
Published London, England Hindawi Publishing Corporation 01.01.2015
SAGE Publications
Sage Publications Ltd. (UK)
Sage Publications Ltd
Hindawi - SAGE Publishing
Subjects
Analysis
Complexity
Compressive properties
Control
Correlation analysis
Electromagnetic interference
Engineering research
Mean square errors
Methods
Noise
Parameter estimation
Reconstruction
Recovery
Samples
Sampling
Signal processing
Sparsity
Statistical analysis
Statistical methods
Theory
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Summary:To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1550-1329
1550-1477
DOI:10.1155/2015/946457