Application of Prozimal Support Vector Regression to Particle Filter

摘要：

An improved particle filter for nonlinear, nonGaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based reweighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this function. Then, posterior density of the state is reapproximated to maintain the effectiveness and diversity of samples. Two experimental results demonstrate that the efficiency of the proposed algorithm compared with the generic particle filter and Markov Chain Monte Carlo (MCMC) particle filter.

关键词： support vector machine prozimal support vector regression particle filter

作者: Wei Jiang Guoxing Yi Qingshuang Zeng

作者单位: Space Control and lnertial Technology Research Center Harbin Institute of Technology Harbin 150001,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 239-243

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）