摘要
针对系统动力学模型不准确可能导致滤波精度下降,以及系统状态协方差阵可能出现的负定性问题,提出一种新的高斯过程回归平方根分解无迹粒子滤波(Gaussian process regression square-root decomposition unscented particle filter,GPSR-UPF)算法。在该算法中,采用高斯过程回归求取UPF的重要性密度函数。当系统模型不准确时,通过高斯过程回归学习训练数据,进而获取系统的回归模型及系统噪声协方差,同时引入平方根变换抑制系统状态协方差阵的负定性。将提出的GPSR-UPF算法应用到捷联惯导/全球定位系统(strapdown inertial navigation system/global positioning system,SINS/GPS)组合导航系统中进行仿真验证。结果表明,所提出滤波算法的性能优于基本的无迹粒子滤波算法,能提高组合导航系统的解算精度。
In view of the uncertainty of the system dynamic model may reduce the filtering effect and the system state eovariance matrix is negative definiteness, a new unscented particle filter（UPF） based on Gaussian process regression and square-root decomposition（GPSR） is proposed. The importance density function of UPF is gotten by Gaussian process regression. When the system model and observation model are inaccurate, Gaussi- an process regression is used to learn the training data, the regression models and noise covariance of the dynam- ic system are gotten; square-root decomposition is used to restrain the negative definiteness of the system state covariance matrix. The proposed algorithm is applied to the integrated navigation system of strapdown inertial navigation system / global positioning system （SINS/GPS）. The simulation results show that the proposed al- gorithm is better than UPF, and also effectively improves the positioning precision of the navigation system.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2015年第12期2817-2822,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61174193)
航天科技创新基金(2014-HTXGD)资助课题
关键词
高斯过程回归
平方根分解
无迹粒子滤波
组合导航系统
Gaussian process regression
square-root decomposition
unscented particle filter （UPF）
integrated navigation
