SISR based Hidden State Estimation of HMMs with transition density function

摘要：

　　Traditional Viterbi algorithm cannot be generally effective.Regarding the hidden state estimates of HMM as a Bayes filtering problem,the Sequential Importance Sampling with Resampling algorithm could get an approximate of its Bayes estimates.Its performance reached or even exceeds the Viterbi algorithm while lower dependence on the model,having a wider range of adaptation.

关键词： HMM MAP SISR

作者: Longteng Li Chengwen Zhu Xiaoyan Cai Chi Zhang Chuizhen Zeng

作者单位: Wuhan Ordnancy Non-commissioned Officer Academy of PLA Wuhan,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1033-1036

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）