Work

Computationally-efficient blind source extraction under extremely low SNR conditions based on principal component analysis

Year: 2025

Type: article

Abstract: Signal extraction under extremely low signal-to-noise ratio (SNR) conditions remains a challenging task in signal processing. Conventional separation and extraction algorithms can achieve satisfactory performance but the computational burden is heavy since they usually involve complicated update rules and require a sufficient number of iterations. In this paper, we prove that, under extremely low SNR conditions, principal component analysis (PCA) can be used as an engineering approximation of the optimal solution for independent vector extraction (IVE). Based on this conclusion, two computationally-efficient methods are proposed to obtain the demixing vector. Both methods involve only a few simple operations and thus require much less computational resources. Numerical experiments across various scenarios demonstrate that the proposed algorithms can achieve comparable performance to conventional algorithms with light computational burden.

Source: Applied Acoustics

DOI: 10.1016/j.apacoust.2025.110709

Authors: Haoxin Ruan, Shang Zhao, Zhibin Lin, Jing Lu

Institutions: Horizon Robotics (China), Nanjing University

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,820

Funders: National Natural Science Foundation of China, Nanjing University