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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20202 - Communication engineering and systems

Project

<a href="/en/project/TM02000035" target="_blank" >TM02000035: NEO classification of signals (NEOCLASSIG) for radio surveillance systems</a><br>

Continuities

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

Publication year

2023

Confidentiality

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.

Name of the periodical

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

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Volume of the periodical

Vol. 89

Issue of the periodical within the volume

Vol. 89 (2023)

Country of publishing house

VN - VIET NAM

Number of pages

9

Pages from-to

43-51

UT code for WoS article

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EID of the result in the Scopus database

999