[1]王少波,郭英,眭萍,等.基于平行因子分析的欠定混合矩阵估计算法[J].探测与控制学报,2019,41(06):101.[doi:.]
 WANG Shaobo,GUO Ying,SUI Ping,et al.Underdetermined Mixed Matrix Estimation Based on Parallel Factor Analysis[J].,2019,41(06):101.[doi:.]
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基于平行因子分析的欠定混合矩阵估计算法()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
41
期数:
2019年06
页码:
101
栏目:
出版日期:
2020-01-15

文章信息/Info

Title:
Underdetermined Mixed Matrix Estimation Based on Parallel Factor Analysis
文章编号:
1008-1194(2019)06-0101-06
作者:
王少波郭英眭萍李红光杨鑫
空军工程大学信息与导航学院, 陕西 西安 710077
Author(s):
WANG Shaobo GUO Ying SUI Ping LI Hongguang YANG Xin
Institute of Information and Navigation,Air Force Engineering University,Xi’an 710077,China
关键词:
欠定盲源分离混合矩阵估计交替最小二乘直接三线性分解标准线搜索
Keywords:
underdetermined blind source separation mixed matrix estimation alternating least squares direct trilinear decomposition standard line search
分类号:
TN975
DOI:
.
文献标志码:
A
摘要:
针对现有算法在解决非稀疏信号的欠定混合矩阵估计中,存在的计算时间长、初值敏感且容易陷入局部收敛的问题,提出了基于平行因子分析的欠定混合矩阵估计算法。该算法利用信号的协方差矩阵构造三阶张量,采用直接三线性分解确定交替最小二乘(ALS)算法的初始迭代矩阵,然后在迭代过程中采用标准线搜索加速收敛,最终实现张量分解得到混合矩阵。仿真实验表明,该方法不要求信源的稀疏性,较ALS算法估计精度可以提高约3 dB,迭代次数减少约41.4%~84.3%,是一种有效的欠定混合矩阵估计算法。
Abstract:
Aiming at the shortcomings of the existing algorithm in solving the under-determined hybrid matrix estimation, and the problems of non-sparse signals, the shortcomings of long calculation time, initial value sensitivity and easy to fall into local convergence are presented, an underdetermined hybrid matrix estimation algorithm based on parallel factor analysis was proposed. The algorithm used the covariance matrix of the signal to construct the third-order tensor, and used the direct trilinear decomposition to determine the initial iterative matrix of the alternating least squares algorithm, and then used the standard line search to accelerate convergence in the iterative process. Simulation results showed that the method did not require the sparseness of the source. The estimation accuracy of the ALS algorithm could be improved by about 3dB, and the number of iterations was reduced by about 41.4%-84.3%.

参考文献/References:

[1]梅铁民.盲源分离理论与算法[M].西安:西安电子科技大学出版社,2004.
[2]焦卫东. 基于独立分量分析的旋转机械故障诊断方法研究[D]. 杭州:浙江大学, 2003.
[3]吴小培, 冯焕清, 周荷琴,等. 基于独立分量分析的图象分离技术及应用[J]. 中国图象图形学报, 2001, 6(2):133-137.
[4]吴小培, 冯焕清, 周荷琴,等. 独立分量分析及其在脑电信号预处理中的应用[J]. 北京生物医学工程, 2001, 20(1):35-37.
[5]杨小牛, 付卫红. 盲源分离——理论、应用与展望[J]. 通信对抗, 2006(3):3-10.
[6]KIM S G, YOO C D. Underdetermined blind source separation based on subspace representation[J]. IEEE Trans. Signal Process., 2009, 57(7): 2604-2614.
[7]LI Y B, NIE W, YE F, et al. A mixing matrix estimation algorithm for underdetermined blind source separation[J]. CircuitsSyst Signal Process, 2016, 35: 3367-3379.
[8]刘琨, 杜利民, 王劲林. 基于时频域单源主导区的盲源欠定分离方法[J]. 中国科学(E辑:信息科学), 2008, 38(8):1284-1301.
[9]Bousse M, Debals O, Lathauwer L De, A tensor-based method for large-scale blind source separation using segmentation[C]//24th European Signal Processing Conference.US:IEEE, 2016: 1-4.
[10]DeLathauwer L, Castaing J. Blind identification of underdetermined mixtures by simultaneous matrix diagonalization[J] IEEE Trans. Signal Proc., 2008,56(3):1096-1105.
[11]ZOU L, CHEN X, JI X Y, et al. Underdetermined uoint blind source separation of nultiple datasets[J]. IEEE Access, 2017, 5: 7474-7487.
[12]杨诚,平行因子分析在多故障源盲分离中的应用研究[D].南昌:南昌航空大学,2018.
[13]张延良, 楼顺天, 张伟涛.欠定盲源分离混合矩阵估计的张量分解方法[J]. 系统工程与电子技术,2011,33(8):1703-1706.
[14]张贤达.矩阵分析与应用[M].北京:清华大学出版社有限公司,2004.
[15]Sidiropoulos N D, Giannakis G B, Bro R. Blind PARAFAC receivers for DS-CDMA systems [J]. IEEE Transactions on Signal Processing, 2000, 48(3): 810-823.
[16]Da Costa M N. Tensor space-time coding for MIMO wirelessommunication systems [D].France: UniversiteNice Sophia Antipolis, 2014.
[17]Comon P, Rajih M. Blind identification of under-determined mixtures based on the characteristic function[J]. Signal Processing, 2006,86(9):2271-2281.
[18]Bro R. Multi-way Analysis in the Food Industry: Models, Algorithms, and Applications[C]//MRI, EPG and EMA, Proc ICSLP 2000.US:ICSLP, 1998.
[19]KIM S G, YOO C D. Underdetermined blind source separation based on subspace representation[J]. IEEE Trans. Signal Process., 2009, 57(7): 2604-2614.
[20]于欣永, 郭英, 张坤峰, 等. 基于盲源分离的多跳频信号网台分选算法[J]. 信号处理, 2017(8):60-67.


备注/Memo

备注/Memo:
收稿日期:2019-08-12
基金项目:国家自然科学基金项目资助(61601500);全军研究生资助课题(JY2018C169)
作者简介:王少波(1994—),男,河北石家庄人,硕士研究生,研究方向:通信信号侦察与处理。E-mail:1120359719@qq.com。

更新日期/Last Update: 2020-01-13