Research on Self-Calibration Method of Underwater Array Error

In the hydroacoustic environment, high-resolution orientation estimation algorithms for sound source localisation are highly sensitive to array errors. How to accurately calibrate the array error has been a major problem in practical engineering. In this paper, we study the underwater array gain and phase error calibration technique for uniform planar arrays, and propose a PTSI-MAP self-calibration method based on an efficient TSI three-step iterative algorithm. In order to verify the proposed algorithm, the embedded software of the array error calibration system based on the TMS320C6678 processor is designed, and the feasibility and effectiveness of the proposed algorithm in the actual underwater environment are proved through pool experiments.

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Publication Details

Published
2025-04-23
DOI
https://doi.org/10.1109/acctcs66275.2025.00024
Primary Topic
Fault Detection and Control Systems
Type
article
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article

Research on Self-Calibration Method of Underwater Array Error

Tiande Gao, Zhao Yizhe
Fault Detection and Control Systems
article

Research on Self-Calibration Method of Underwater Array Error

Tiande Gao, Zhao Yizhe
article en

Abstract

In the hydroacoustic environment, high-resolution orientation estimation algorithms for sound source localisation are highly sensitive to array errors. How to accurately calibrate the array error has been a major problem in practical engineering. In this paper, we study the underwater array gain and phase error calibration technique for uniform planar arrays, and propose a PTSI-MAP self-calibration method based on an efficient TSI three-step iterative algorithm. In order to verify the proposed algorithm, the embedded software of the array error calibration system based on the TMS320C6678 processor is designed, and the feasibility and effectiveness of the proposed algorithm in the actual underwater environment are proved through pool experiments.

Northwestern Polytechnical University (CN)
Openalex Percentile: Top 71%
Fault Detection and Control Systems
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