CAJ | 학술논문

依据星座图采用非参数贝叶斯方法对多元相移键控(MPSK)信号进行调制识别.将未知信噪比(SNR)水平的MPSK信号看成复平面内多个未知均值和方差的高斯分布依照一定的比例混合而成,利用非参数贝叶斯推断方法进行密度估计,实现对MPSK信号分类目的.推断过程中,引入Dirichlet过程作为混合比例因子的先验分布,结合正态逆Wishart(NIW)分布作为均值和方差的先验分布,根据接收信号,利用Gibbs采样的MCMC(Monte Carlo Markov chain)随机采样算法,不断调整混合比例因子、均值和方差.通过多次迭代,得到对调制信号的密度估计.仿真表明,在SNR>5 dB,码元数目大于1 600时,2/4/8PSK的识别率超过了95%.
의거성좌도채용비삼수패협사방법대다원상이건공(MPSK)신호진행조제식별.장미지신조비(SNR)수평적MPSK신호간성복평면내다개미지균치화방차적고사분포의조일정적비례혼합이성,이용비삼수패협사추단방법진행밀도고계,실현대MPSK신호분류목적.추단과정중,인입Dirichlet과정작위혼합비례인자적선험분포,결합정태역Wishart(NIW)분포작위균치화방차적선험분포,근거접수신호,이용Gibbs채양적MCMC(Monte Carlo Markov chain)수궤채양산법,불단조정혼합비례인자、균치화방차.통과다차질대,득도대조제신호적밀도고계.방진표명,재SNR>5 dB,마원수목대우1 600시,2/4/8PSK적식별솔초과료95%.
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.