Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148967
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSha, Weijiaen_US
dc.date.accessioned2021-05-29T07:17:30Z-
dc.date.available2021-05-29T07:17:30Z-
dc.date.issued2021-
dc.identifier.citationSha, W. (2021). Study of radar signature extraction for effective gesture classification with machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148967en_US
dc.identifier.urihttps://hdl.handle.net/10356/148967-
dc.description.abstractModern radar technology has various types of applications and brings convenience to people from different perspectives. Radar gesture recognition can be one of them. With the help of machine learning, it can reach a reliable classification and recognition rate. This project is aimed to compare the influence of different radar spectrogram feature extraction methods on recognition accuracy, focusing on finding the suitable feature extraction method under different scenarios and with different machine learning algorithms. This report summarizes the knowledge of micro-Doppler radar, image processing, feature extraction method as well as training algorithms. As a result, principal component analysis (PCA) together with AlexNet produced the best accuracy up to 100% with certain hand gesture radar spectrogram dataset.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3144-201en_US
dc.subjectEngineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radioen_US
dc.titleStudy of radar signature extraction for effective gesture classification with machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLU Yilongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailEYLU@ntu.edu.sgen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
SHA_WEIJIA_FYP_FullReport.pdf
  Restricted Access
2.63 MBAdobe PDFView/Open

Page view(s)

80
Updated on May 18, 2022

Download(s)

6
Updated on May 18, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.