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Sparse spatial spectral estimation in directional noise environment.

Long Yang¹, Yixin Yang¹, Yong Wang¹

¹School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, China ylong311@126.com, yxyang@nwpu.edu.cn, yongwang.nwpu@gmail.com.

The Journal of the Acoustical Society of America

|December 5, 2016

Summary

This summary is machine-generated.

This study introduces a new linear noise model and directional noise field sparse spectrum fitting (DN-SpSF) algorithm for spatial spectral estimation. The DN-SpSF algorithm improves direction of arrival estimation in non-ideal, directional noise environments.

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Area of Science:

Signal Processing
Array Signal Processing
Acoustic Signal Processing

Background:

The assumption of White Gaussian noise is prevalent in spatial spectral estimation.
Practical ambient noise often deviates from this assumption, degrading direction of arrival (DOA) estimation performance.
Existing methods struggle with spatially correlated or directional noise fields.

Purpose of the Study:

To develop a more realistic noise model for ambient noise fields.
To propose a novel algorithm for spatial spectral estimation in directional noise.
To enhance the accuracy of direction of arrival estimation under non-ideal noise conditions.

Main Methods:

Introduction of a linear noise model to characterize directional noise.
Development of the directional noise field sparse spectrum fitting (DN-SpSF) algorithm.
Validation through simulations and experimental data analysis.

Main Results:

The proposed linear noise model provides a more accurate description of practical noise.
The DN-SpSF algorithm demonstrates superior performance in spatially directional noise environments.
Significant improvements in direction of arrival estimation accuracy were observed.

Conclusions:

The DN-SpSF algorithm effectively addresses the limitations of the White Gaussian noise assumption.
This approach offers a robust solution for spatial spectral estimation in complex noise scenarios.
The findings are crucial for improving DOA estimation in real-world applications.