DOA-CNN: An automatic system error calibration model for enhancing accuracy in direction of arrival estimation of radio frequency signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25547526%3A_____%2F23%3AN0000004" target="_blank" >RIV/25547526:_____/23:N0000004 - isvavai.cz</a>

Výsledek na webu

<a href="https://ojs.jmst.info/index.php/jmst/article/view/888" target="_blank" >https://ojs.jmst.info/index.php/jmst/article/view/888</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.54939/1859-1043.j.mst.89.2023.43-51" target="_blank" >10.54939/1859-1043.j.mst.89.2023.43-51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DOA-CNN: An automatic system error calibration model for enhancing accuracy in direction of arrival estimation of radio frequency signals

Popis výsledku v původním jazyce

This paper presents a research proposal on a deep convolutional neural network for the problem of direction of arrival estimation of radio frequency signals (called DOA-CNN). The DOA-CNN model is designed with multiplication layers to enhance strong features of the data through convolutional stacks enabling the DOA classification accuracy. The evaluation considers several factors affecting the accuracy of DOA estimation for uniform linear array (ULA), including antenna element position errors, and amplitude and phase errors caused by transmission path deviations in the receiver. The analysis and comparison of DOA-CNN with CBF, Capon, MUSIC, Root-MUSIC, and ESPRIT methods and other machine learning methods show that, considering the ideal configuration of the ULA array and the receiver, the Root-MUSIC and ESPRIT methods achieve the best accuracy since they can directly compute the DOA, while the other methods estimate the DOA via angular spectrum, leading to accuracy dependent on the spectral resolution. However, considering ULA errors and transmission path deviations in the receiver, the proposed DOA-CNN model outperforms in terms of accuracy compared to traditional methods and processes faster than some other machine learning models.

Název v anglickém jazyce

DOA-CNN: An automatic system error calibration model for enhancing accuracy in direction of arrival estimation of radio frequency signals

Popis výsledku anglicky

This paper presents a research proposal on a deep convolutional neural network for the problem of direction of arrival estimation of radio frequency signals (called DOA-CNN). The DOA-CNN model is designed with multiplication layers to enhance strong features of the data through convolutional stacks enabling the DOA classification accuracy. The evaluation considers several factors affecting the accuracy of DOA estimation for uniform linear array (ULA), including antenna element position errors, and amplitude and phase errors caused by transmission path deviations in the receiver. The analysis and comparison of DOA-CNN with CBF, Capon, MUSIC, Root-MUSIC, and ESPRIT methods and other machine learning methods show that, considering the ideal configuration of the ULA array and the receiver, the Root-MUSIC and ESPRIT methods achieve the best accuracy since they can directly compute the DOA, while the other methods estimate the DOA via angular spectrum, leading to accuracy dependent on the spectral resolution. However, considering ULA errors and transmission path deviations in the receiver, the proposed DOA-CNN model outperforms in terms of accuracy compared to traditional methods and processes faster than some other machine learning models.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/TM02000035" target="_blank" >TM02000035: Pokroková klasifikace signálů (NEOCLASSIG) pro radio-průzkumné systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Název periodika

DOA-CNN: An automatic system error calibration model for enhancing accuracy in direction of arrival estimation of radio frequency signals

ISSN

1859-1043

e-ISSN

—

Svazek periodika

Vol. 89

Číslo periodika v rámci svazku

Vol. 89 (2023)

Stát vydavatele periodika

VN - Vietnamská socialistická republika

Počet stran výsledku

9

Strana od-do

43-51

Kód UT WoS článku

—

EID výsledku v databázi Scopus

999