Independent Vector Analysis Based MIMO Deconvolution: Exploiting Spatial Diversity Through Back Projection

被引:0
|
作者
Belouchrani, Adel [1 ]
Mendjel, Nacira [1 ]
Berrah, Lynda [1 ]
Tebache, Soufiane [1 ]
机构
[1] Ecole Natl Polytech, Dept Elect Engn, LDCCP, Algiers, Algeria
来源
2023 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP, SSP | 2023年
关键词
Back projection; Minimal Distortion Principle; Independent Vector Analysis; SIMO Deconvolution; ICA;
D O I
10.1109/SSP53291.2023.10207982
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper deals with the scaling ambiguity issue in blind convolutive source separation when performed in the frequency domain. It considers two major techniques, mainly the Minimal Distortion Principle and the Back Projection, that allow to overcome the aforementioned indeterminacy. The main contribution of this paper consists of exploiting one of the most beneficial outcomes of the Back Projection, which is spatial diversity. Our proposed approach applies Single Input Multiple Output deconvolution to the outputs of the back projected source signals, after their estimation by the Independent Vector Analysis algorithm. This method has the advantage of both improving the estimation accuracy and removing the channel effect. Experimental results show the effectiveness of our proposal, compared to the traditional used Minimum Distortion Principle.
引用
收藏
页码:478 / 481
页数:4
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