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Sequential sensor selection for the localization of acoustic sources by sparse Bayesian learning.

Milan Courcoux-Caro¹, Charles Vanwynsberghe², Cédric Herzet³

¹ENSTA Bretagne, Lab-STICC UMR 6285 CNRS, 2 Rue François Verny, 29806 Brest Cedex 09, France.

The Journal of the Acoustical Society of America

|October 1, 2022

Summary

This summary is machine-generated.

This study introduces a sequential sensor selection method for acoustic source localization using sparse approximation. The approach optimizes sensor placement to improve localization accuracy and robustness, outperforming existing methods.

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Area of Science:

Acoustics
Signal Processing
Array Signal Processing

Background:

Sparse approximation is effective for acoustic source localization.
Sensor array design significantly impacts localization accuracy and depends on factors like sensor count and geometry.
Existing methods often require offline processing and can be sensitive to environmental factors.

Purpose of the Study:

To develop an online, sequential sensor selection procedure for acoustic source localization.
To optimize sensor array design for improved sparse approximation-based localization.
To enhance robustness against model mismatches caused by environmental reflections.

Main Methods:

A sequential sensor selection procedure alternating between source localization estimation and optimal sensor placement.
Utilizing estimation variance to guide the selection of the next measurement point.
Online application of the sensor selection algorithm.
Numerical and experimental studies in an indoor nearfield environment.

Main Results:

The proposed online method outperforms offline state-of-the-art methods in acoustic source localization.
Empirical studies demonstrate superior robustness to model mismatches arising from room reflections.
The sequential selection procedure effectively optimizes sensor array configuration.

Conclusions:

The developed sequential sensor selection method offers an effective online strategy for designing sensor arrays for acoustic source localization.
The approach enhances localization accuracy and robustness, particularly in environments with reflections.
This work provides a valuable tool for optimizing sensor configurations in practical acoustic sensing applications.