Blind signal separation based on widely linear complex autoregressive process of order one

Blind signal separation based on widely linear complex autoregressive process of order one

Abstract In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of general...

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Bibliographic Details
Main Authors: Jiong Li, Yuan Qin, Menglan Fan, Xiaogang Tang, Lijuan Gao, Long Chen, Junhao Feng
Format: Article
Language:English
Published: SpringerOpen 2021-02-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Blind source separation
Complex auto-regressive model
Generalized autocorrelation
Gradient learning
Online Access:https://doi.org/10.1186/s13638-021-01920-8
Description
Summary:Abstract In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.
ISSN:1687-1499

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