FMICW Radar Target Classification By Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39916106" target="_blank" >RIV/00216275:25530/20:39916106 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA49387.2020.9092342" target="_blank" >http://dx.doi.org/10.1109/RADIOELEKTRONIKA49387.2020.9092342</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA49387.2020.9092342" target="_blank" >10.1109/RADIOELEKTRONIKA49387.2020.9092342</a>

Jazyk výsledku

angličtina

Název v původním jazyce

FMICW Radar Target Classification By Neural Network

Popis výsledku v původním jazyce

This document describes automatic classification of targets detected by the FMICW radar. These targets are counted and sorted to three groups (incoming, outgoing and static targets). We derived this information from the output of the neural network which marked the targets in 2D spectrum. The additional neural network has five layers. The first layer is used for the suppression of the targets with even numbers of points, which causes problems during the symmetry detection. The second and third layers detect the symmetry in the dimension (vertical or horizontal). The fourth layer checks out if the symmetry is in both dimensions and if the detection is not a false alert caused by the constellation of the targets. The fifth layer contains only 4 neurons and this layer is used for counting of the targets and classification of the targets (if they are static, incoming or outgoing). The neural network is composed of a simple block for the easy implementation on the FPGA.

Název v anglickém jazyce

FMICW Radar Target Classification By Neural Network

Popis výsledku anglicky

This document describes automatic classification of targets detected by the FMICW radar. These targets are counted and sorted to three groups (incoming, outgoing and static targets). We derived this information from the output of the neural network which marked the targets in 2D spectrum. The additional neural network has five layers. The first layer is used for the suppression of the targets with even numbers of points, which causes problems during the symmetry detection. The second and third layers detect the symmetry in the dimension (vertical or horizontal). The fourth layer checks out if the symmetry is in both dimensions and if the detection is not a false alert caused by the constellation of the targets. The fifth layer contains only 4 neurons and this layer is used for counting of the targets and classification of the targets (if they are static, incoming or outgoing). The neural network is composed of a simple block for the easy implementation on the FPGA.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

30th International Conference Radioelektronika, RADIOELEKTRONIKA 2020

ISBN

978-1-72816-469-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Bratislava

Datum konání akce

15. 4. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—