Recognition and Classification of Direct Spread Spectrum Signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F25%3A00563645" target="_blank" >RIV/60162694:G43__/25:00563645 - 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/NTSP61680.2024.10726301" target="_blank" >10.23919/NTSP61680.2024.10726301</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recognition and Classification of Direct Spread Spectrum Signals

Popis výsledku v původním jazyce

This paper deals with the design and implementation of a direct spread spectrum signal classifier. The Binary-Phase Shift Keying (BPSK) and Quadrature Phase-Shift Keying (QPSK) modulation were used to create the signal patterns and Pseudo-Random (PR) sequences, Gold codes and Kasami codes were used to directly spread the spectrum of the modulated signals. The properties of the proposed direct spread spectrum signal models were simulated for Signal-to-Noise Ratio (SNR) from 0 to 10 dB for different spread factor to make their properties close to real direct spread spectrum signals. The Welch's power spectral density estimation method, wavelet transform and signal autocorrelation detection method were used for conceptual design and implementation of the direct spread spectrum signal classifier in Matlab programming environment.

Název v anglickém jazyce

Recognition and Classification of Direct Spread Spectrum Signals

Popis výsledku anglicky

This paper deals with the design and implementation of a direct spread spectrum signal classifier. The Binary-Phase Shift Keying (BPSK) and Quadrature Phase-Shift Keying (QPSK) modulation were used to create the signal patterns and Pseudo-Random (PR) sequences, Gold codes and Kasami codes were used to directly spread the spectrum of the modulated signals. The properties of the proposed direct spread spectrum signal models were simulated for Signal-to-Noise Ratio (SNR) from 0 to 10 dB for different spread factor to make their properties close to real direct spread spectrum signals. The Welch's power spectral density estimation method, wavelet transform and signal autocorrelation detection method were used for conceptual design and implementation of the direct spread spectrum signal classifier in Matlab programming environment.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic 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

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Demanovska Dolina, Slovakia

Místo konání akce

Demanovska Dolina, Slovak Republic

Datum konání akce

16. 10. 2024

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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