Unscented particle filter based Gaussian process regression for IMU/BDS train integrated positioning
Aiming at the inaccurate of system dynamic model may reduce the filtering effect,a new Unscented Particle Filter based on improved Gaussian process (GPUPF) is proposed in this paper.The importance density function of UPF is obtained by Gaussian process regression,when the system model and noise are uncertain,GPR is used to revise and estimate the system,determine the covariance matrices of system noises,gets better importance density function,and enhance the adaptive capability of UPF.The improved algorithm was applied to the IMU/BDS train integrated positioning system.Simulation results show that the proposed algorithm is better than standard UPF,leading to improved positioning precision.
Gaussian process regression nonlinear filtering unscented particle filter IMU/BDS integration
Meng Yang Gao Shesheng Wang Wei
Northwestern Polytechnical University Xian,China
国际会议
重庆
英文
1070-1073
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）