A New Method for Polyphase Coded Radar Signals Recognition Based on Dual Channel Deep Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F25%3A00564304" target="_blank" >RIV/60162694:G43__/25:00564304 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10726284" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10726284</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/NTSP61680.2024.10726319" target="_blank" >10.23919/NTSP61680.2024.10726319</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A New Method for Polyphase Coded Radar Signals Recognition Based on Dual Channel Deep Learning

Popis výsledku v původním jazyce

This paper presents a new method for recognizing polyphase coded radar signals based on dual-channel deep learning. The proposed method includes two stages. In the first step, the Morse Wavelet and continuous Wavelet transforms are used to extract signal features in the time and time-frequency domain. In the second, dual-channel deep learning is designed to recognize radar signals. The first channel uses the extracted feature in the time-frequency domain, and the second uses the phase change of the signals in the time domain. The proposed method's performance is evaluated with simulated signals in a MATLAB environment. The simulation results approved that the proposed method provides a higher accuracy recognition than other methods, which only use single-channel deep learning with signal feature extraction in the time-frequency domain.

Název v anglickém jazyce

A New Method for Polyphase Coded Radar Signals Recognition Based on Dual Channel Deep Learning

Popis výsledku anglicky

This paper presents a new method for recognizing polyphase coded radar signals based on dual-channel deep learning. The proposed method includes two stages. In the first step, the Morse Wavelet and continuous Wavelet transforms are used to extract signal features in the time and time-frequency domain. In the second, dual-channel deep learning is designed to recognize radar signals. The first channel uses the extracted feature in the time-frequency domain, and the second uses the phase change of the signals in the time domain. The proposed method's performance is evaluated with simulated signals in a MATLAB environment. The simulation results approved that the proposed method provides a higher accuracy recognition than other methods, which only use single-channel deep learning with signal feature extraction in the time-frequency domain.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the International Conference on New Trends in Signal Processing, NTSP 2024

ISBN

978-80-8040-637-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

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Místo konání akce

Demanovska Dolina, Slovak Republic

Datum konání akce

16. 10. 2024

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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