东南大学学报（英文版）2009，Vol.25Issue(2)：171-174,4.
基于非参数贝叶斯推断的MPSK信号调制识别
Modulation classification of MPSK signals based on nonparametric Bayesian inference
摘要
Abstract
A nonparametric Bayesian method is presented to classify the MPSK (M-ary phase shift keying) signals. The MPSK signals with unknown signal noise ratios (SNRs) are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart (NIW) distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain (MCMC) random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR >5 dB and 1 600 symbols are used in this method.关键词
调制分类/多元相移键控/Dirichlet过程/非参数贝叶斯推断/Monte Carlo Markov chainKey words
modulation classification/M-ary phase shift keying/Dirichlet process/nonparametric Bayesian inference/MonteCarlo Markov chain分类
信息技术与安全科学引用本文复制引用
陈亮,程汉文,吴乐南..基于非参数贝叶斯推断的MPSK信号调制识别[J].东南大学学报（英文版）,2009,25(2):171-174,4.基金项目
Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China( No. 3104001014). （ No. 3104001014）
