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Related Experiment Video

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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

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Near-Field Source Localization in Nonuniform Noise: An Efficient Symmetric Matrix Factorization-Based Approach.

Wenze Song¹, Zhenqing He^1,2, Guohao Sun^1,3

¹School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

This study presents a robust method for near-field source localization under unknown nonuniform noise. The novel approach accurately estimates signal sources and noise variances, outperforming existing techniques.

Keywords:

majorization–minimization near-field source localization nonuniform noise symmetric matrix factorization

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Area of Science:

Signal Processing
Array Signal Processing
Electromagnetics

Background:

Near-field source localization is crucial for various applications.
Existing methods often struggle with unknown nonuniform noise.
Noise heterogeneity can significantly degrade localization accuracy.

Purpose of the Study:

To develop a robust near-field source localization method for multiple narrowband signals.
To address the challenge of unknown nonuniform noise with arbitrary diagonal covariance.
To improve localization accuracy and robustness in complex noise environments.

Main Methods:

Reformulated the problem as joint symmetric matrix factorization and noise variance estimation.
Developed an iterative algorithm based on the block majorization-minimization principle.
Derived the Cramér-Rao bound (CRB) for near-field localization under nonuniform noise.

Main Results:

The proposed algorithm achieves high accuracy in near-field source localization.
Demonstrated superior performance compared to state-of-the-art methods.
The approach shows strong robustness against severe nonuniform noise.

Conclusions:

The novel covariance-fitting approach effectively handles nonuniform noise.
The developed iterative algorithm is computationally efficient.
The method provides near-optimal performance, closely matching the derived CRB.