Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148967
Title: Study of radar signature extraction for effective gesture classification with machine learning
Authors: Sha, Weijia
Keywords: Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Issue Date: 2021
Publisher: Nanyang Technological University
Source: 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
Project: A3144-201
Abstract: 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.
URI: https://hdl.handle.net/10356/148967
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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