Document Type Master's Dissertation Author Du Plessis, Marthinus Christoffel URN etd-09092010-202158 Document Title Non-stationary signal classification for radar transmitter identification Degree MEng Department Electrical, Electronic and Computer Engineering Supervisor
Advisor Name Title Prof J C Olivier Committee Chair Keywords
- non-stationary signal classification
- wavelet packet transform
- Wigner-Ville transform
- support vector machine
- Battle-Lemariť wavelet
- quadratic time-frequency representation
- discrete wavelet transform
- multiresolution analysis
Date 2010-09-02 Availability unrestricted Abstract
The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation.
Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohenís class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator.
Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohenís class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier.
The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohenís class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohenís class classifier.
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Please cite as follows:
Du Plessis, MC 2010, Non-stationary signal classification for radar transmitter identification, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-09092010-202158/ >
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