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中文摘要：
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，S1NS／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。
英文摘要：
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.
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Rapid alignment method based on local observability analysis for strapdown inertial navigation syste
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