计算机工程2017，Vol.43Issue(10)：62-67,6.DOI:10.3969/j.issn.1000-3428.2017.10.011
结合Tucker张量分解与交替最小二乘的ULA盲识别
ULA Blind Identification Combining Tucker Tensor Decomposition and Alternating Least Squares
摘要
Abstract
In order to improve the computational efficiency of Blind Identification (BI) of Uniform Linear Array (ULA) system,an improved ULA BI algorithm is proposed.Firstly,the signal propagation model of ULA system is established.Then,the algebraic structure and parameter estimation method of the Generalized Generating Function (GGF) are given.Secondly,it uses alternating least squares to obtain GGF of ULA system,and then uses the Tucker tensor decomposition to improve alternating least squares,achieving dimensional reduction of GGF.Experimental results show that,compared with classic DUET algorithm and underdetermined aliasing BI decomposition algorithm,the proposedalgorithm has higher computational efficiency and better ULA BI effects.关键词
Tucker张量分解/广义生成函数/交替最小二乘/均匀线性阵列/盲识别Key words
Tucker tensor decomposition/Generalized Generating Function (GGF)/alternating least squares/Uniform Linear Array (ULA)/Blind Identification (BI)分类
信息技术与安全科学引用本文复制引用
胡丹,郭英杰..结合Tucker张量分解与交替最小二乘的ULA盲识别[J].计算机工程,2017,43(10):62-67,6.基金项目
贵州省科技计划项目(黔科合LH字[2014]7627). （黔科合LH字[2014]7627）
