Specific emitter identification refers to the process of performing identification of radio
frequency transmitters by exploiting unique variations in emitted signals, caused
by hardware variations. In previous research, specific emitter identification was successfully
performed on GSM handsets. However, no research has been done on the
implementation of specific emitter identification of GSM handsets on an FPGA platform.
This study focuses on feature extraction and identification algorithms, as well
as the implementation of the identification algorithm on an FPGA.
During this study, phase modulation error was used, as previous research indicated
that phase modulation error is an effective feature set for identification purposes.
As the implementation of a classification algorithm on an FPGA was required, a
trade-off between complexity and feasibility needed to be made during the selection
process. The artificial neural network was selected as the optimal classifier for
implementation on an FPGA. The algorithm was first implemented in software and
used as the basis for the design on an FPGA. A piece-wise linear approximation of a
sigmoid function was used to approximate the activation function, where a look-up
table was used to store the parameters.
The off-line training of the artificial neural network was performed in software using
the back-propagation gradient descent algorithm.
Good results for the identification of GSM handsets on an FPGA were obtained, with
a true acceptance ratio of 97.0%. This result is similar to the performance obtained
in previous research performed in software. In this study, it was found that specific
emitter identification of GSM handsets can be performed on an FPGA. Real-world
applications for this technology include general cellular handset identification and
access control. / MSc (Electrical and Electronic Engineering), North-West University, Potchefstroom Campus, 2015
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/15283 |
| Date | January 2015 |
| Creators | Hattingh, Johannes Petrus |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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