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Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction.

Authors :
Chen, Liang
Liu, Jiayu
Source :
Journal of Marine Science & Engineering; May2024, Vol. 12 Issue 5, p810, 22p
Publication Year :
2024

Abstract

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20771312
Volume :
12
Issue :
5
Database :
Complementary Index
Journal :
Journal of Marine Science & Engineering
Publication Type :
Academic Journal
Accession number :
177493323
Full Text :
https://doi.org/10.3390/jmse12050810