Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157516
Title: AI radar (object classification using deep learning)
Authors: Li, Jianhui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Li, J. (2022). AI radar (object classification using deep learning). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157516
Project: A3279-211
Abstract: In this project, we aim to use the self-collected datasets which is fully labelled to train a Convolutional Neural Network (CNN) to reduce the computation cost and improve performance accuracy, to classify the targets detected by the radar as human beings or nonhuman objects, using the range-Doppler maps. For the radar used in this project, it is a frequency modulated continuous wave (FMCW) radar. The whole project can be divided into three parts. Firstly, Design and implementation of collecting training data process. Secondly, training data preprocessing using MATLAB. And lastly, the construction of Convolutional Neural Network based on a VGG-11 backbone using PyTorch. Results show the encouraging improvement on the classification accuracy.
URI: https://hdl.handle.net/10356/157516
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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