Degree Name

Master of Engineering by Research


School of Electrical, Computer and Telecommunications Engineering


Traditionally, the Doppler radar is an effective tool for detecting the position and velocity of a moving target, even in adverse weather conditions and from a long range. In recent years, radar systems have been used to detect and identify targets of interest due to their various advantages. Classification, recognition, and identification of targets andmotion kinematics basedonmicro-Doppler signatures have become an emerging research field with numerous civilian and military applications.

This project investigates the automatic classification systemof humanmotions using a Doppler radar. The radar signals are obtained by using a frequency modulated continuouswave radar to scanmoving targets. The short time Fourier transformis used to convert the radar signal into spectrogramto provide the timevarying frequency information. Window function types, sizes and overlapping rate of the short time Fourier transform are explored to provide higher resolution for the spectrogram. Intensity transformation and thresholding techniques are applied on the spectrograms to enhance the weak micro-Doppler signatures and remove the background noise.

To identify the movement of a target using a Doppler radar, extraction and analysis of prominent micro-Doppler features from the spectrogram are important. Instead of processing the entire spectrograms, local windows are detected to reduce redundant information and provide features that are invariant to the target’s speed. Local window alignment method is also investigated since misaligned images produce severe artifacts in scattermatrices of principal component analysis. Based on the local windows, the new two-directional, two-dimensional principal component analysis and GIST methods are performed to obtain feature vectors. The support vector machine with RBF kernel is used to classify the feature vectors into motion types. The proposed two-directional, two-dimensional principal component analysis and GIST approaches achieve classification rate of 97.8% and 98.5%, respectively. To compare with the proposed method, the traditional 1-D PCA and HICA are also tested on the same database, and they reach classification rates of 97.6% and 97.7%, respectively.