%0 Journal Article
%T A Novel Bayesian Modulation Classification Algorithm<br>一种新的贝叶斯调制分类算法
%A Liu Zheng
%A Wang Ming-yang
%A Jiang Wen-li
%A Zhou Yi-yu
%A <br>柳 征
%A 王明阳
%A 姜文利
%A 周一宇
%J 电子与信息学报
%D 2006
%I 
%X A novel method is proposed for digital modulation classification based on Markov chain Monte Carlo (MCMC). Considering the difficulty for Bayesian classifier with unknown residual carrier phase and frequency, marginal likelihood probability density is estimated by Metropolis-Hastings (M-H) algorithm, which kept the theoretical optimality and robustness of Bayesian classifier. The simulated results show that the novel classifier outperforms the one based on cumulants.
%K Markov Chain Monte Carlo (MCMC)
%K Modulation classification
%K Bayesian classifier
%K Metropolis-Hastings (M-H) algorithm
%K Marginal likelihood function<br>马尔可夫链蒙特卡罗(MCMC)
%K 调制分类
%K 贝叶斯分类器
%K Metropolis-Hastings(M-H)算法
%K 边缘似然函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=0F967F01D615CF70&yid=37904DC365DD7266&vid=D3E34374A0D77D7F&iid=DF92D298D3FF1E6E&sid=603BC00D7DC5FEAC&eid=72EB001A9B3C78CE&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=11