Adaptive Gaussian Particle Filter for Nonlinear State Estimation

摘要：

The Gaussian particle filter has emerged as a useful tool for nonlinear state estimation problems. The sample size used in the estimation is one of the key factors to the efficiency and accuracy of the filter. However, the fixed sample size which is usually determined empirically may be highly inappropriate since it ignores the varying errors of the processes. This paper presents a sample size adaptive Gaussian particle filter that uses statistical methods and unscented transform technique to estimate the needed sample size in the time update step and the observation update step respectively at each iteration. Simulation results show that the proposed method performs much better than the standard GPF in the nonlinear problems with great accuracy.

关键词： Gaussian Particle Filter Nonlinear State Estimation Sample Size Adaption Unscented Transform

作者: KONG Liang KONG Lingfu WU Peiliang

作者单位: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China Colleg College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 2146-2150

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）