Micro-Doppler Effect and Determination of Rotor Blades by Deep Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F23%3A00558038" target="_blank" >RIV/60162694:G43__/23:00558038 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/22:PU144529

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9764897" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9764897</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA54537.2022.9764934" target="_blank" >10.1109/RADIOELEKTRONIKA54537.2022.9764934</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Micro-Doppler Effect and Determination of Rotor Blades by Deep Neural Networks

Popis výsledku v původním jazyce

The paper deals with the analysis of simulated data, where thousands of samples of reflections from a radar target, a helicopter, with propellers were simulated. Simulations were performed for helicopters with 3, 4, 6, and 8 propeller blades. Data collection and evaluation were focused on the measurement of the Doppler Effect, specifically the Micro-Doppler effect for the rotating propeller section. The simulations have been divided into several sections for all types of helicopters differing in the number of propellers. The most considered was the change of Radar Cross Section (RCS), but changes in helicopter movement speed, changes in helicopter position relative to the radar, and changes in helicopter rotation speed have been considered as well. Moreover, a simulation of the change in radar carrier frequency across the microwave band was performed and the changes and effects on the Micro-Doppler measurement data were studied. However, the main task of this paper was to determine the number of propeller blades from any simulated signal sample with parameters corresponding to the Micro-Doppler, which was successfully done. Simulated data has been used to train a deep learning network to classify the number of propeller blades on a randomly selected measured/simulated sample. To detect the number of rotors, we have chosen to use Convolutional Neural Networks (CNN), which achieve good results for object recognition from images.

Název v anglickém jazyce

Micro-Doppler Effect and Determination of Rotor Blades by Deep Neural Networks

Popis výsledku anglicky

The paper deals with the analysis of simulated data, where thousands of samples of reflections from a radar target, a helicopter, with propellers were simulated. Simulations were performed for helicopters with 3, 4, 6, and 8 propeller blades. Data collection and evaluation were focused on the measurement of the Doppler Effect, specifically the Micro-Doppler effect for the rotating propeller section. The simulations have been divided into several sections for all types of helicopters differing in the number of propellers. The most considered was the change of Radar Cross Section (RCS), but changes in helicopter movement speed, changes in helicopter position relative to the radar, and changes in helicopter rotation speed have been considered as well. Moreover, a simulation of the change in radar carrier frequency across the microwave band was performed and the changes and effects on the Micro-Doppler measurement data were studied. However, the main task of this paper was to determine the number of propeller blades from any simulated signal sample with parameters corresponding to the Micro-Doppler, which was successfully done. Simulated data has been used to train a deep learning network to classify the number of propeller blades on a randomly selected measured/simulated sample. To detect the number of rotors, we have chosen to use Convolutional Neural Networks (CNN), which achieve good results for object recognition from images.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

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)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 32nd International Conference Radioelektronika, RADIOELEKTRONIKA 2022 - Proceedings

ISBN

978-1-7281-8686-3

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Košice - Radioelektronika 2022

Místo konání akce

Kosice, Slovak Republic

Datum konání akce

21. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—