A Clustering Algorithm for Multitarget State Extraction Based on Probability Hypotheses Density

摘要：

This paper proposes a clustering algorithm for multitarget state extraction based on Finite Mixture Models (FMM) and Probability Hypotheses Density (PHD). The PHD gives the number of targets. The FMM clustering algorithm extracts multitarget states. The FMM parameters are derived by Gibbs sampling under the Bayesian framework, besides, the algorithm join the prior information, thus the tracking property will be more accurate.

关键词： PHD Clustering of Gaussian mixture model Finite mixture models Gibbs sampling Bayesian

作者: Weifeng Liu Chongzhao Han Feng Lian

作者单位: Electronic Information Engr Xian Jiaotong University Xian, Shaan xi 710049,P.R.China

会议类型: 国际会议

会议名称: The International Colloquium on Onformation Fusion 2007(2007年国际信息融合研讨会)

会议地点: 西安

会议语种:英文

页码: 204-212

在线出版日期: 2007-08-22（万方平台首次上网日期，不代表论文的发表时间）