Study of radar signature extraction for effective gesture classification with machine learning
Modern 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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148967 |
| _version_ | 1826120355538796544 |
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| author | Sha, Weijia |
| author2 | LU Yilong |
| author_facet | LU Yilong Sha, Weijia |
| author_sort | Sha, Weijia |
| collection | NTU |
| description | Modern 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. |
| first_indexed | 2024-10-01T05:15:09Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148967 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:15:09Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1489672023-07-07T18:25:53Z Study of radar signature extraction for effective gesture classification with machine learning Sha, Weijia LU Yilong School of Electrical and Electronic Engineering EYLU@ntu.edu.sg Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio Modern 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-29T07:17:30Z 2021-05-29T07:17:30Z 2021 Final Year Project (FYP) Sha, 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/10356/148967 https://hdl.handle.net/10356/148967 en A3144-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio Sha, Weijia Study of radar signature extraction for effective gesture classification with machine learning |
| title | Study of radar signature extraction for effective gesture classification with machine learning |
| title_full | Study of radar signature extraction for effective gesture classification with machine learning |
| title_fullStr | Study of radar signature extraction for effective gesture classification with machine learning |
| title_full_unstemmed | Study of radar signature extraction for effective gesture classification with machine learning |
| title_short | Study of radar signature extraction for effective gesture classification with machine learning |
| title_sort | study of radar signature extraction for effective gesture classification with machine learning |
| topic | Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio |
| url | https://hdl.handle.net/10356/148967 |
| work_keys_str_mv | AT shaweijia studyofradarsignatureextractionforeffectivegestureclassificationwithmachinelearning |