Modulation characterization using the wavelet transform, 1997
Watkins, Lanier A.
1990-1999
The focus of this research is to establish an Automatic Modulation Identifier (AMI) using the Continuous Wavelet Transform (CWT) and several different classifiers. A Modulation Identifier is of particular interest to the military, because it has the potential to quickly discriminate between different communication waveforms. The CWT is used to extract characterizing information from the signal, and an artificial Neural Network is trained to identify the modulation type. Various analyzing wavelets and various classifiers were used to assess comparative performance. The analyzing wavelets used were the Mexican Hat Wavelet, the Morlet Wavelet, and the Haar Wavelet. The variety of classifiers used were the Multi-Layer Perceptron, the K-Nearest Neighbor and the Fuzzy Artmap. The CWT served as a preprocessor, and the classifiers served as an identifier for Binary Phase Shift Keying (BPSK), Binary Frequency Shift Keying (BFSK), Binary Amplitude Shift Keying (BASK), Quadature Phase Shift Keying (QPSK), Eight Phase Shift Keying (8PSK), and Quadature Amplitude Modulation (QAM) signals. Separation of BASK, BFSK and BPSK was performed in part one of the research project, and separation of BPSK, QPSK, 8PSK, BFSK, and QAM comprised the second part of the project. Each experiment was performed for waveforms corrupted with Additive White Gaussian Noise ranging from 20 dB - 0 dB carrier to noise ratio (CNR). To test the robustness of the technique, part one of the research project was tested upon several carrier frequencies w/2, and w/3 which was different from the carrier frequency w that the classifiers were trained upon. In the separation of BASK, BFSK and BPSK, the AMI worked extremely well (100% correct classification) down to 5 dB CNR tested at carrier frequency w, and it worked well (80% correct classification) down to 5 dB CNR tested at carrier frequencies w/2, and w/3. In the separation of BPSK, QPSK, 8PSK, BFSK, and QAM, the AMI performed very well at 10 dB CNR (98.8% correct classification). Also a hardware design in the Hewlet Packard Visual Engineering Environment (HP-VEE) for implementation of the AMI algorithm was constructed and is included for future expansion of the project.
text
application/pdf
1997-05-01
thesis
Master of Science (MS)
Clark Atlanta University
School of Arts and Sciences, Physics
Perry, Kenneth K.
Georgia--Atlanta
http://hdl.handle.net/20.500.12322/cau.td:1997_watkins_lanier_a
