Target Signal Extraction Method Based on Enhanced ICA with Reference

Liuyang Gao,Jingzhi Zhang,Yinghua Tian,Nae Zheng

doi:10.1155/2019/4128438

Liuyang Gao, Jingzhi Zhang + Show 2 more

Open Access

https://doi.org/10.1155/2019/4128438

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Abstract

Target signal extraction has a great potential for applications. To solve the problem of error extraction of target signals in the current constrained independent component analysis (cICA) method, an enhanced independent component analysis with reference (EICA-R) method is proposed. The new algorithm establishes a unified cost function, which combines the negative entropy contrast function and the distance metric function. The EICA-R method transforms the constrained optimization problem into unconstrained optimization problem to overcome the problem of threshold setting of distance metric function in constrained optimization problem. The theoretical analysis and simulation experiment show that the proposed EICA-R algorithm overcomes the problem of the error extraction of the existing algorithm and improves the reliability of the target signal extraction.

Highlights

Target signal extraction is used to extract unknown source signals from multiple linear mixed signals, which has found a wide range of applications
In the enhanced independent component analysis (ICA) with reference (EICA-R) proposed in this paper, a priori information is directly contained in the ICA framework combined with the negative entropy contrast function and target signal distance metric function
Direction 1: combining the distance metric function and the negative entropy contrast function, we reduce the constrained conditions and establish two forms of cost functions F(w), according to the different transformation forms of max J(y): ε(y, r)

Summary

Introduction

Target signal extraction is used to extract unknown source signals from multiple linear mixed signals, which has found a wide range of applications. ICA can effectively separate all the signals, including target signals, interference signals, and background noise in the non-underdetermined case, which is widely used in audio signal processing [5], mechanical engineering [6], and biomedical diagnosis [7,8,9]. If there is a frequency aliasing between the signals, the signal cannot be separated by the traditional filtering method In this case, the constrained ICA (cICA) algorithm [17,18,19], incorporating prior information, can be used to extract the target signal [20, 21]. In the enhanced ICA with reference (EICA-R) proposed in this paper, a priori information is directly contained in the ICA framework combined with the negative entropy contrast function and target signal distance metric function.

Mixed Signal Separation and cICA

Enhanced ICA with Reference

Simulation Experiment and Performance Analysis

Conclusion