基于张量分析的欠定混合矩阵估计算法

马宝泽; 李国军; 向翠玲; 徐阳

doi:10.11959/j.issn.1000-436x.2022206

您当前的位置：

首页 >

文章列表页 >

基于张量分析的欠定混合矩阵估计算法

学术论文 | 更新时间：2024-06-05

- 基于张量分析的欠定混合矩阵估计算法
- Underdetermined mixing matrix estimation algorithm based on tensor analysis
- 通信学报 2022年43卷第11期页码：35-43
- 作者机构：
  
  1. 重庆邮电大学光电工程学院，重庆 400065
  2. 重庆邮电大学超视距可信信息传输研究所，重庆 400065
  3. 重庆邮电大学光电信息感测与传输技术重庆市重点实验室博士后科研工作站，重庆 400065
- 作者简介：
  
  [ "马宝泽（1990− ），男，河北廊坊人，博士，重庆邮电大学讲师，主要研究方向为盲源分离、信道辨识、数据分析、深度学习等" ]
  [ "李国军（1978− ），男，四川资阳人，博士，重庆邮电大学教授、博士生导师，主要研究方向为复杂恶劣环境超视距无线通信与网络" ]
  [ "向翠玲（1996− ），女，重庆人，重庆邮电大学硕士生，主要研究方向为信道估计与均衡、短波建链技术" ]
  [ "徐阳（1998− ），男，湖南常德人，重庆邮电大学硕士生，主要研究方向为信道估计与均衡、自适应迭代算法" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2019YFC1511300);国家自然科学基金资助项目(62201113);重庆市重点研发基金资助项目(cstc2017zdcy-zdyfX0011)
- DOI：10.11959/j.issn.1000-436x.2022206
  中图分类号： TN911.6
- 网络出版日期：2022-11，
  
  纸质出版日期：2022-11-25
- 稿件说明：
移动端阅览
马宝泽, 李国军, 向翠玲, 等. 基于张量分析的欠定混合矩阵估计算法[J]. 通信学报, 2022,43(11):35-43.

Baoze MA, Guojun LI, Cuiling XIANG, et al. Underdetermined mixing matrix estimation algorithm based on tensor analysis[J]. Journal on communications, 2022, 43(11): 35-43.
马宝泽, 李国军, 向翠玲, 等. 基于张量分析的欠定混合矩阵估计算法[J]. 通信学报, 2022,43(11):35-43. DOI： 10.11959/j.issn.1000-436x.2022206.

Baoze MA, Guojun LI, Cuiling XIANG, et al. Underdetermined mixing matrix estimation algorithm based on tensor analysis[J]. Journal on communications, 2022, 43(11): 35-43. DOI： 10.11959/j.issn.1000-436x.2022206.

摘要

针对欠定矩阵估计中存在有效特征信息提取难和算法收敛速度慢等问题，提出基于张量分析的瞬时混合欠定矩阵估计算法，旨在克服信号稀疏性约束。该算法通过信号分割子段的自协方差构造对称三阶张量，并压缩为核张量降低数据规模，利用增强线性搜索技术加速交替最小二乘算法的收敛速度，将因子矩阵作为混合矩阵估计的测度，但分割子段数选取是个开放问题。仿真表明，所提算法在估计欠定混合矩阵时性能优于稀疏变换法和传统高阶统计量法。

Abstract

Aiming at the problems of difficult to extract effective feature information and the slow convergence speed of the underdetermined matrix estimation

an underdetermined matrix estimation algorithm of instantaneous mixtures based on tensor analysis was proposed to overcome the constraint of signal sparsity.In the proposed algorithm

the symmetric third-order tensor was constructed via the autocovariance matrix of segmentation sub-block

which was compressed into a kernel tensor to reduce the size of the data.An enhanced line search technology was applied to speed up the convergence of alternating least squares method

and the factor matrix was used as the measure of the mixing matrix estimation

but the selection of the number of segmentation sub-blocks was an open problem.Experimental results demonstrate that the proposed algorithm outperforms the sparse transformation method and the traditional high-order statistical method in handling the underdetermined mixing matrix estimation.

关键词

Keywords

references

BOUSSÉ M , DEBALS O , LATHAUWER L D . A tensor-based method for large-scale blind source separation using segmentation [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 2 ): 346 - 358 .

MA B Z , ZHANG T Q , AN Z L , et al . Measuring dependence for permutation alignment in convolutive blind source separation [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2022 , 69 ( 3 ): 1982 - 1986 .

马宝泽 , 张天骐 , 安泽亮 , 等 . 基于张量分解的卷积盲源分离方法 [J ] . 通信学报 , 2021 , 42 ( 8 ): 52 - 60 .

MA B Z , ZHANG T Q , AN Z L , et al . Convolutive blind source separation method based on tensor decomposition [J ] . Journal on Communications , 2021 , 42 ( 8 ): 52 - 60 .

LAWAL A , MAYYALA Q , ABED-MERAIM K , , et al . Blind signal estimation using structured subspace technique [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2021 , 68 ( 8 ): 3007 - 3011 .

XIE Y , XIE K , XIE S L . Underdetermined blind separation of source using lp-norm diversity measures [J ] . Neurocomputing , 2020 , 411 : 259 - 267 .

MA B Z , ZHANG T Q . Underdetermined blind source separation based on source number estimation and improved sparse component analysis [J ] . Circuits,Systems,and Signal Processing , 2021 , 40 ( 7 ): 3417 - 3436 .

ZHANG M J , YU S M , WEI G . Sequential blind identification of underdetermined mixtures using a novel deflation scheme [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2013 , 24 ( 9 ): 1503 - 1509 .

YANG L , ZHANG H , CAI Y . A low-complexity PARAFAC decomposition for underdetermined blind system identification with complex mixtures [J ] . Circuits,Systems,and Signal Processing , 2018 , 37 ( 11 ): 4842 - 4860 .

SMITH S , PISCHELLA M , TERRÉ M , . A moment-based estimation strategy for underdetermined single-sensor blind source separation [J ] . IEEE Signal Processing Letters , 2019 , 26 ( 6 ): 788 - 792 .

ABRARD F , DEVILLE Y . A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources [J ] . Signal Processing , 2005 , 85 ( 7 ): 1389 - 1403 .

LATHAUWER L D , CASTAING J , CARDOSO J F . Fourth-order cumulant-based blind identification of underdetermined mixtures [J ] . IEEE Transactions on Signal Processing , 2007 , 55 ( 6 ): 2965 - 2973 .

LATHAUWER L D , CASTAING J . Blind identification of underdetermined mixtures by simultaneous matrix diagonalization [J ] . IEEE Transactions on Signal Processing , 2008 , 56 ( 3 ): 1096 - 1105 .

COMON P , RAJIH M . Blind identification of under-determined mixtures based on the characteristic function [J ] . Signal Processing , 2006 , 86 ( 9 ): 2271 - 2281 .

BOUSSÉ M , DEBALS O , LATHAUWER L D . Tensor-based large-scale blind system identification using segmentation [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 21 ): 5770 - 5784 .

GUAN W , DONG L L , ZHOU J M , et al . Tensor-based approach for underdetermined operational modal identification [J ] . Mechanical Systems and Signal Processing , 2021 ,160:107891.

ZHAO R Q , WANG Q . Learning separable dictionaries for sparse tensor representation:an online approach [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2019 , 66 ( 3 ): 502 - 506 .

RAJIH M , COMON P , HARSHMAN R A . Enhanced line search:a novel method to accelerate PARAFAC [J ] . SIAM Journal on Matrix Analysis and Applications , 2008 , 30 ( 3 ): 1128 - 1147 .

COMON P , LUCIANI X , DE-ALMEIDA A L F , . Tensor decompositions,alternating least squares and other tales [J ] . Journal of Chemometrics , 2009 , 23 ( 9 ): 393 - 405 .

COMON P . Tensors:a brief introduction [J ] . IEEE Signal Processing Magazine , 2014 , 31 ( 3 ): 44 - 53 .

KOLDA T , BADER B . Tensor decompositions and applications [J ] . SIAM Review , 2009 , 51 ( 3 ): 455 - 500 .

SIDIROPOULOS N D , DE LATHAUWER L , FU X , et al . Tensor decomposition for signal processing and machine learning [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 13 ): 3551 - 3582 .

浏览量

298

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据