A Recovery Algorithm of MWC Sub-Nyquist Sampling Based on Random Projection Method

GAI Jian-xin; FU Ping; SUN Ji-yu; LIN Hai-jun; WU Li-hua

doi:10.3969/j.issn.0372-2112.2014.09.004

您当前的位置：

首页 >

文章列表页 >

A Recovery Algorithm of MWC Sub-Nyquist Sampling Based on Random Projection Method

更新时间：2025-07-16

- A Recovery Algorithm of MWC Sub-Nyquist Sampling Based on Random Projection Method
- Acta Electronica Sinica Vol. 42, Issue 9, Pages: 1686-1692(2014)
- 作者机构：
  
  1. 哈尔滨理工大学测控技术与仪器黑龙江省高校重点实验室,黑龙江,哈尔滨,150080
  3. 哈尔滨工业大学自动化测试与控制系,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  Science and Technology Research Project of Education Department of Heilongjiang Province (No.12531133)
- DOI：10.3969/j.issn.0372-2112.2014.09.004
  CLC： TN911.7
- Published：2014
- 稿件说明：
移动端阅览
GAI Jian-xin, FU Ping, SUN Ji-yu, et al. A Recovery Algorithm of MWC Sub-Nyquist Sampling Based on Random Projection Method[J]. Acta Electronica Sinica, 2014, 42(9): 1686-1692.
DOI：

GAI Jian-xin, FU Ping, SUN Ji-yu, et al. A Recovery Algorithm of MWC Sub-Nyquist Sampling Based on Random Projection Method[J]. Acta Electronica Sinica, 2014, 42(9): 1686-1692. DOI： 10.3969/j.issn.0372-2112.2014.09.004.

摘要

针对现有调制宽带转换器（Modulated Wideband Converter，MWC）亚奈奎斯特采样重构算法性能不高问题，提出了一种基于随机投影思想的重构算法.该算法首先将MWC所获得的测量值矩阵通过随机投影方法压缩成具有较少向量的新的测量值矩阵，然后利用所提出的求解器求解多测量向量问题，通过检验和重复尝试性求解过程提高MWC的重构性能.从理论和实验两个方面验证了所提出的算法的有效性.实验结果表明，与著名的ReMBo算法相比，该算法有效提高了重构成功率；当信号的频带数相同时，精确重构所需的硬件通道数更小；在相同的硬件通道数前提下，可重构的信号频带数更高.该算法与ReMBo相比运算时间并没有大幅度增加，当信号频带数较大时，不仅重构性能高，而且运算时间比ReMBo小.

Abstract

The existing recovery algorithm of modulated wideband converter (MWC)-based sub-Nyquist sampling is far from satisfactory.Aiming at this problem

a recovery algorithm for MWC based on random projection method is proposed.This algorithm projects the measurement value matrix of MWC onto a random matrix with lower dimension to form a new measurement value matrix

and then solves a multiple measurement vector problem using a solver proposed.The recovery performance is enhanced through examining and repeating the tentative solving processes.This paper validates the effectiveness of the algorithm from both theoretical and experimental perspectives.Numerical experiments demonstrate that

compared with the popular ReMBo algorithm

the proposed algorithm significantly improves the success rate of recovery.From the same number of channels a signal with more spectral bands can be recovered by this algorithm

and a signal with the same number of bands can be recovered using fewer channels.Furthermore

the run time of this algorithm does not increase greatly.In contrast

compared with ReMBo

this algorithm can use a lower time cost to achieve a higher recovery performance when the spectral bands of the signal exceed a specific number.

关键词

Keywords

references

Views

2135

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Traffic Object Recognition in Complex Scenes Based on SIFT and Kernel Sparse Representation

Research on Joint Source-Channel Coding Method Based on Deep Compressive Sensing

Time-Domain Analyses of Quadrature Compressive Sampling System

Non-Convex Compressive Sensing Ultra-Wide Band Channel Estimation Method in Low SNR Conditions

Related Author

WANG Rui

DU Lin-feng

SUN Du

WAN Wang-gen

DONG Hao

LI Shao-hui

KAN Nuo-wen

ZHENG Zi-yang

Related Institution

School of Communication and Information Engineering, Shanghai University

Tsinghua Shenzhen International Graduate School

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

School of Electronic and Optical Engineering， Nanjing University of Science and Technology

School of Electronic Engineering， Nanjing Xiaozhuang University

⁰