Please use this identifier to cite or link to this item:
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.
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.
Schools: School of Electrical and Electronic Engineering 
Research Centres: Satellite Research Centre 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Final_Report_Li Jianhui.pdf
  Restricted Access
1.65 MBAdobe PDFView/Open

Page view(s)

Updated on Dec 8, 2023

Download(s) 50

Updated on Dec 8, 2023

Google ScholarTM


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