Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

Research on Recognition Methods of underwater acoustic signal based on higher-order statistics

Authors
Zhenyu Li, Zhiqiang Chen, Bo Zhang, Zhihui Zhang, Ziyang Yu
Corresponding Author
Zhenyu Li
Available Online May 2018.
DOI
https://doi.org/10.2991/amcce-18.2018.13How to use a DOI?
Keywords
Higher order statistics; Bispectrum; Recognition; Typical correlation analysis method.
Abstract
In acoustic signal recognition problem, different analysis methods can extract different characteristics for the same goal. Good feature fusion methods can take advantage of different traits of each feature, complement each other ,remove redundancy, get more robust new features, and improve algorithm recognition rate. This method can also complete the data compression dimensionality reduction and improve the real-time algorithm. This shows that feature fusion is extraordinary. This paper presents a signal recognition method based on high order statistics and power spectrum estimation and theoretical simulation results. This method can be used to recognize underwater acoustic signals and has a high recognition rate.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.13How 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  - Zhenyu Li
AU  - Zhiqiang Chen
AU  - Bo Zhang
AU  - Zhihui Zhang
AU  - Ziyang Yu
PY  - 2018/05
DA  - 2018/05
TI  - Research on Recognition Methods of underwater acoustic signal based on higher-order statistics
BT  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.13
DO  - https://doi.org/10.2991/amcce-18.2018.13
ID  - Li2018/05
ER  -