Thesis (MScEng)--Stellenbosch University, 2014. / 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. / AFRIKAANSE OPSOMMING: Hierdie werk behels die ontwikkeling, en analise van werksverrigting, van
’n menslike stapdetekor gebaseer op radar-mikrodoppleranalise. Die stelsel
bestaan uit ’n teikenvolger en -klassifiseerder. Die mikrodoppler-kenmerke
van ’n teiken word met behulp van die korttyd-Fourier-transform onttrek,
en verskaf hoe-resolusie-kenmerke met die radar wat vir die implementering
gebruik word. ’n Kenmerkontrekkingstelsel is ontwikkel om ses kenmerke
vanuit die spektrogram te onttrek, en ’n kunsmatige neurale netwerk word
as klassifiseerder gebruik. Die stelsel is met ’n X-band radar op werklike
menslike beweging getoets, terwyl vrywilligers ses aktiwiteite uitgevoer het:
loop, loop (hand in die sakke), marsjeer, hardloop, loop met ’n wapen, loop
met arms wat swaai. Die multiklas-klassifiseerder is ontwerp om tussen hierdie
aktiwiteite te onderskei. ’n Hoe klassifiseringsakkuraatheid van 96%
word gedemonstreer.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/86652 |
| Date | 04 1900 |
| Creators | Alzogaiby, Adel |
| Contributors | Van Rooyen, G-J., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | xiv, 83 p. : ill. |
| Rights | Stellenbosch University |
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