Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology

Normal-Mode Based MUSIC for Bearing Estimation in Shallow Water Using Acoustic Vector Sensors

Authors
Weiwei Ai, Jinyu Xiong, Xiaoyong Zhang
Corresponding Author
Weiwei Ai
Available Online August 2016.
DOI
https://doi.org/10.2991/icmeit-16.2016.19How to use a DOI?
Keywords
Acoustic vector sensor array, Normal-Mode based MUSIC, Shallow ocean, Unbiased bearing estimates.
Abstract
To realize unbiased bearing estimates of multiple acoustic sources in a range-independent shallow water, Normal-Mode based MUSIC (NM-MUSIC) method using acoustic vector sensor (AVS) array is proposed in this paper. Comparing to NM-MUSIC method based on scalar array, the method based on AVS array solves the problem of port and starboard ambiguity, and also breaks through the limitation of half wavelength. Meanwhile, the method realizes unbiased bearing estimates, while most of the conventional Direction of Arrival (DOA) methods result in biased bearing estimates in shallow water. Simulation results show that the performance of the new method proposed is better than that of the original method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Mechatronics Engineering and Information Technology
Part of series
Advances in Engineering Research
Publication Date
August 2016
ISBN
978-94-6252-222-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmeit-16.2016.19How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Weiwei Ai
AU  - Jinyu Xiong
AU  - Xiaoyong Zhang
PY  - 2016/08
DA  - 2016/08
TI  - Normal-Mode Based MUSIC for Bearing Estimation in Shallow Water Using Acoustic Vector Sensors
BT  - 2016 International Conference on Mechatronics Engineering and Information Technology
PB  - Atlantis Press
SP  - 104
EP  - 109
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeit-16.2016.19
DO  - https://doi.org/10.2991/icmeit-16.2016.19
ID  - Ai2016/08
ER  -