A New Quasi-Monte Carlo Filtering Algorithm Based on Number Theoretical Method

摘要：

To improve the filtering precision when dealing with the state estimation problem of nonlinear/non-Gaussian systems, we propose a novel sequential quasi-Monte Carlo (SQMC) filtering algorithm which is analogous to the sequential Monte Carlo (SMC) or particle filtering methods. The central idea of the new algorithm is to apply one of the deterministic sampling methods, i.e., number theoretic sampling method to SQMC. The point set of uniform distribution generated by cyclotomic field can construct more uniform scattered points in unit cube. Therefore, random samples generated by the point set of uniform distribution can adequately describe the posterior probability density function (PDF). Simulation results show that the proposed filtering algorithm provides better performance in nonlinear/non-Gaussian state estimation when compared to classical particle filter, SQMC using Halton sequence in presence of severe nonlinearity.

关键词： Nonlinear state estimation Cyclotomic field Point set of uniform distribution Sequential quasi-Monte Carlo

作者: Hui Zhang Chongzhao Han

作者单位: Department of Electronic and Information EngineeringXian Jiaotong UniversityXian,Shaan xi Province Department of Electronic and Information Engineering Xian Jiaotong University Xian,Shaan xi Provin

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）