Title :
Modulation Classification Based on Multifractal Features
Author :
Tao, He ; Zheng-ou, Zhou ; Li Xi-rong
Author_Institution :
Dept. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu
Abstract :
A novel method for modulation classification is proposed. The multifractal dimensions of signal, which have preferable distinction capability and robustness to SNR variance, are extracted as the distinctive features. These features are obtained based on the phase space reconstructing theory and the classifier is built by adopting probabilistic neural network. Simulations on CW, BPSK, BFSK, 4ASK and 16QAM signals indicate that the proposed method can classify the modulated signals accurately
Keywords :
feature extraction; modulation; neural nets; probability; signal classification; signal reconstruction; distinctive feature extraction; modulation classification; multifractal feature; phase space reconstructing theory; probabilistic neural network; Anisotropic magnetoresistance; Binary phase shift keying; Electronic warfare; Feature extraction; Fractals; Helium; Interference; Neural networks; Noise robustness; Signal processing;
Conference_Titel :
ITS Telecommunications Proceedings, 2006 6th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
0-7803-9587-5
Electronic_ISBN :
0-7803-9587-5
DOI :
10.1109/ITST.2006.288821
