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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144529" target="_blank" >RIV/00216305:26220/22:PU144529 - isvavai.cz</a>

  • Alternative codes found

    RIV/60162694:G43__/23:00558038

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9764934" target="_blank" >https://ieeexplore.ieee.org/document/9764934</a>

  • DOI - Digital Object Identifier

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

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20202 - Communication engineering and systems

Result continuities

  • Project

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

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA)

  • ISBN

    978-1-7281-8686-3

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    1-6

  • Publisher name

    IEEE

  • Place of publication

    neuveden

  • Event location

    Košice

  • Event date

    Apr 21, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article