U-GPF INFORMATION FUSION ALGORITHM FOR GPS/DR INTEGRATED POSITIONING SYSTEM

摘要：

U-GPF is proposed for GPS/DR integrated positioning system to improve its performance.It is based on the Gaussian Particle Filter (GPF) and Unscented Kalman Filter (UKF).UKF is used to calculate the estimate parameters value and covariance matrix in the observation update, and the distribution function is sampled as the importance density function for GPF.Simulation results show that U-GPF and UKF has similar accuracy on the Gaussian noise, but they are better than Extended Kalman Filter (EKF).However, for the non-Gaussian noise, U-GPF has higher accuracy than UKF and EKF.The collected real data is applied to validate the U-GPF and the results are consistent with the theory analysis and simulation result.

关键词： Information fusion Particle filter Unscented Kalman Filter Global positioning system

作者: DONC-KAI YANG XIN-LI ZHOU XU LIU QI-SHAN ZHANG

作者单位: School of Electronic and Information Engineering, Beihang University, Beijing 100083, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1424-1427

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）