(Stellenbosch : Stellenbosch University, 2014-04) Alzogaiby, Adel; Van Rooyen, G-J.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
ENGLISH ABSTRACT: This work entails the development and performance analysis of a human gait
detection system based on radar micro-Doppler signals. The system consists
of a tracking functionality and a target classifier. Target micro-Doppler
signatures are extracted with Short-Time Fourier Transform (STFT) based
spectrogram providing a high-resolution signatures with the radar that is
used. A feature extraction mechanism is developed to extract six features
from the signature and an artificial neural network (A-NN) based classifier is
designed to carry out the classification process. The system is tested on real
X-band radar data of human subjects performing six activities. Those activities
are walking and speed walking, walking with hands in pockets, marching,
running, walking with a weapon, and walking with arms swaying. The multiclass
classifier was designed to discriminate between those activities. High
classification accuracy of 96% is demonstrated.