Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25547526%3A_____%2F22%3AN0000001" target="_blank" >RIV/25547526:_____/22:N0000001 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9943003" target="_blank" >https://ieeexplore.ieee.org/document/9943003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ATC55345.2022.9943003" target="_blank" >10.1109/ATC55345.2022.9943003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array

Popis výsledku v původním jazyce

Direction of arrival (DOA) estimation plays a crucial role in radio signal surveillance and reconnaissance systems because it provides spatial information to localize radiated signal sources. Conventional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant technique (ESPRIT), are very sensitive to defects of antenna arrays that reduce the accuracy of estimated DOA in real applications. To mitigate this issue, an auto-encoder based on U-Net is proposed to transfer the imperfect covariance matrix to a new one; then, the MUSIC algorithm is applied to the new covariance matrix to estimate the DOAs of incoming signals. The proposed approach is investigated through simulation for a uniform linear array of eight elements with an inter-element space of half-wavelength. The simulation results indicate that our proposed method achieves a good performance in terms of DOA estimation accuracy. In comparison, the proposed model has outperformed the other models, such as conventional MUSIC, ESPRIT, and two other deep neural networks.

Název v anglickém jazyce

Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array

Popis výsledku anglicky

Direction of arrival (DOA) estimation plays a crucial role in radio signal surveillance and reconnaissance systems because it provides spatial information to localize radiated signal sources. Conventional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant technique (ESPRIT), are very sensitive to defects of antenna arrays that reduce the accuracy of estimated DOA in real applications. To mitigate this issue, an auto-encoder based on U-Net is proposed to transfer the imperfect covariance matrix to a new one; then, the MUSIC algorithm is applied to the new covariance matrix to estimate the DOAs of incoming signals. The proposed approach is investigated through simulation for a uniform linear array of eight elements with an inter-element space of half-wavelength. The simulation results indicate that our proposed method achieves a good performance in terms of DOA estimation accuracy. In comparison, the proposed model has outperformed the other models, such as conventional MUSIC, ESPRIT, and two other deep neural networks.

Druh

D - Stať ve sborníku

CEP obor

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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

2022

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 statě ve sborníku

Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array

ISBN

978-1-6654-5188-8

ISSN

2162-1039

e-ISSN

2162-1020

Počet stran výsledku

5

Strana od-do

413-417

Název nakladatele

IEEE

Místo vydání

Ha Noi, Vietnam

Místo konání akce

Ha Noi, Vietnam

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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