A New Algorithm to Estimate Mixing-Matrix of Underdetermined Blind Signal Separation

Author

Cui Zhi-Tao ; Jian Ke

Author_Institution

Dept. of Comput. & Inf. Sci., City Coll. of Dong guan Univ. of Technol., Dongguan, China

fYear

2012

fDate

17-18 Nov. 2012

Firstpage

374

Lastpage

377

Abstract

The paper puts forward a new algorithm to estimate mixing-matrix according to the underdetermined blind signal separation of 3 observed signals and 4 sources. According to the geometric meaning of the SCA model, the paper analyzes the numerical feature of the observed signal and proves that the inner product under Euclidean space can be used to classify the observed signal in the situation. Besides, the paper gives a method for determining the number of source signal and introduces an estimation algorithm for mixing-matrix using inner products in the Euclidean space combined with the density of interval point. The algorithm can effectively identify the number of source signals and can realize the estimation of mixing-matrix. The experimental results show the algorithm is feasible.

Keywords

blind source separation; matrix algebra; Euclidean space; SCA model; interval point density; mixing-matrix estimation algorithm; observed signals; source signal; underdetermined blind signal separation; Algorithm design and analysis; Blind source separation; Clustering algorithms; Estimation; Signal processing algorithms; Sparse matrices; Vectors; BSS; Euclidean space; K-means algorithm; SCA; inner product;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2012 Eighth International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4673-4725-9

Type

conf

DOI

10.1109/CIS.2012.90

Filename

6405948