Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs

Shi, Fan, Chen, Zhenlei and Cheng, Xiaochun ORCID logoORCID: (2020) Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs. IEEE Access, 8 . pp. 2447-2454. ISSN 2169-3536 [Article] (doi:10.1109/ACCESS.2019.2923059)

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It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D Power Amplifier, a typical non-linear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of
power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. Firstly, the non-linearity of the sonar transmitter is studied in depth, and the nonlinearity of the power amplifier is modeled and its non-linearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and then the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the non-linear characteristics of the signal. By this way, this method can enhance the communication safety of UASNs.

Item Type: Article
Additional Information: ESSN: 2169-3536
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 26818
Notes on copyright: (c) 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
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Depositing User: Xiaochun Cheng
Date Deposited: 17 Jun 2019 11:37
Last Modified: 14 Jun 2022 05:03

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