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

FAN Fu-hua; RUAN Huai-lin

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

更新时间：2025-07-16

- Non-Convex Compressive Sensing Ultra-Wide Band Channel Estimation Method in Low SNR Conditions
- Acta Electronica Sinica Vol. 42, Issue 2, Pages: 353-359(2014)
- 作者机构：
  
  解放军电子工程学院,安徽,合肥,230037
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61171170)
- DOI：
  CLC： TN911.72
- Published：2014
- 稿件说明：
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FAN Fu-hua, RUAN Huai-lin. Non-Convex Compressive Sensing Ultra-Wide Band Channel Estimation Method in Low SNR Conditions[J]. Acta Electronica Sinica, 2014, 42(2): 353-359.
DOI：

FAN Fu-hua, RUAN Huai-lin. Non-Convex Compressive Sensing Ultra-Wide Band Channel Estimation Method in Low SNR Conditions[J]. Acta Electronica Sinica, 2014, 42(2): 353-359. DOI：

摘要

受感知信息算子矩阵相干性和噪声的影响，压缩感知超宽带（UWB）信道估计误差过大.为此，首先提出利用压缩观测信号加权构造自适应感知信息（ASI）算子矩阵的方法，ASI算子矩阵不仅具有弱相干性，而且包含观测信息，适用于重建算法选择最优稀疏表示原子.其次提出修正稀疏度自适应匹配追踪（SAMP）算法，无需稀疏度或信噪比的先验信息实现压缩感知稀疏信号准确重建.最后基于ASI算子矩阵和修正SAMP算法提出非凸化压缩感知UWB信道估计方法，理论分析和仿真结果均表明该方法能在低信噪比和极低压缩比下实现UWB信道的准确估计.

Abstract

There could be large error in Compressive Sensing Ultra-Wide Band (CS-UWB) channel estimation because of the coherence of sensing information matrix and the noise interference.Firstly

the Adaptive Sensing Information (ASI) matrix is constructed by measurement vector weighting

which has the desirable properties of low coherence and containing measurement information to support the sparse recovery algorithms to obtain optimal atoms.Secondly

a modified Sparsity Adaptive Matching Pursuit (SAMP) algorithm is proposed to recovery sparse signal accurately without prior information of sparsity or SNR.Finally

a non-convex CS-UWB channel estimation method is presented based on the ASI matrix and the modified SAMP algorithm.Both the theoretical analysis and the experimental results show that this method can accurately reconstruct UWB communication channel in low SNR conditions with very low compressive sampling ratio.

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