Neural Networks Application for Processing of the Data from the FMICW Radars
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F19%3A39914421" target="_blank" >RIV/00216275:25530/19:39914421 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2073-8994/11/10/1308/pdf" target="_blank" >https://www.mdpi.com/2073-8994/11/10/1308/pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/sym11101308" target="_blank" >10.3390/sym11101308</a>
Alternative languages
Result language
angličtina
Original language name
Neural Networks Application for Processing of the Data from the FMICW Radars
Original language description
In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted ContinuousWave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Symmetry
ISSN
2073-8994
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
10
Country of publishing house
CH - SWITZERLAND
Number of pages
15
Pages from-to
1-15
UT code for WoS article
000495457600122
EID of the result in the Scopus database
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