西安电子科技大学学报（自然科学版）2016，Vol.43Issue(5)：116-120,5.DOI:10.3969/j.issn.1001-2400.2016.05.021
广义生成函数张量分解的欠定混合盲辨识
Tensor decomposition of generalized generating function-based blind identification of underdetermined mixtures
摘要
Abstract
Aimed at the problem of underdetermined blind identification , an algorithm based on generalized generating function decomposition is proposed , which no longer imposes sparsity restrictions on source signals . First , the second derivative matrices of the generalized generating function are stacked to the third‐order tensor form , from which the number of source signals can be blindly estimated . Then the tensor is decomposed with singular value decomposition , and the mixture matrix is estimated by the joint diagonalization method . Simulation results validate the effectiveness of the proposed algorithm , and show that the proposed algorithm can acquire a better estimation precision than other classical algorithms with the same SNRs in the conditions of well‐posed and underdetermined mixtures , meanwhile it extends the field of blind source separation application via the generalized generating function restricted only to the well‐posed case .关键词
欠定盲辨识/广义生成函数/张量分解/联合对角化/稀疏分量分析Key words
underdetermined blind identification/general generating function/tensor decomposition/joint diagonalization/sparse component analysis分类
信息技术与安全科学引用本文复制引用
周志文,黄高明,高俊..广义生成函数张量分解的欠定混合盲辨识[J].西安电子科技大学学报（自然科学版）,2016,43(5):116-120,5.基金项目
国家“863”高技术研究发展计划资助项目 （）
