Steered sample algorithm for acoustic source localization.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 09 04 2020
accepted: 08 10 2020
entrez: 26 10 2020
pubmed: 27 10 2020
medline: 27 10 2020
Statut: epublish

Résumé

High-precision source localization depends on many factors, including a suitable location method. Beamforming-based methods, such as the steered response power (SRP), are a common type of acoustic localization methods. However, these methods have low spatial resolution. The SRP method with phase transform (SRP-PHAT) improves the spatial resolution of SRP and is one of the most effective and robust methods for source localization. However, the introduction of a phase transform to SRP might amplify the power of the noise and result in many local extrema in the SRP space, which has a negative impact on source localization. In this paper, a steered sample algorithm (SSA) based on the reciprocity of wave propagation for acoustic source localization is proposed. The SSA localization process is similar to the hyperbolic Radon transform, which is theoretically analyzed and is the most essential difference form the SRP/SRP-PHAT. Compared with the SRP-PHAT, the experimental results demonstrate that the SSA perform better when it comes to array signal positioning with limited array elements and narrow azimuth signal, where SSA can achieve high precision positioning with lower SNR.

Identifiants

pubmed: 33105477
doi: 10.1371/journal.pone.0241129
pii: PONE-D-20-08045
pmc: PMC7588096
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0241129

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

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Auteurs

Bin Liu (B)

Shanxi Key Laboratory of Information Survey & Processing, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.

Lichao Zhang (L)

School of Information and Communication Engineering, North University of China, Taiyuan, China.

Pengfei Nie (P)

Shanxi Key Laboratory of Information Survey & Processing, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.

Xingcheng Han (X)

Shanxi Key Laboratory of Information Survey & Processing, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.

Yan Han (Y)

Shanxi Key Laboratory of Information Survey & Processing, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.

Classifications MeSH