中文摘要：

研究组合导航系统精度优化问题。针对粒子滤波存在重要性密度函数难以选取的问题，提出一种新的自适应GHPF算法，通过高斯一厄米特滤波来获取状态均值和协方差阵，计算自适应因子并利用自适应因子调节均值和方差，得到一种参数可调节的重要性密度函数。重要性密度函数考虑了最新量测的影响，提高了滤波精度，使滤波性能明显改善，能更好地解决非线性非高斯系统模型的滤波问题。将提出的算法应用于SINS／SAR组合导航系统中，仿真结果表明，提出的滤波算法能提高导航计算的精度，定位性能明显优于与扩展Kalman滤波、粒子滤波以及高斯～厄米特粒子滤波。

英文摘要：

Aiming at the particle filtering problem that is difficult to select the importance density function, this paper presented an adaptive Gauss-Hermite particle filtering （GHPF） algorithm by adopting Gauss-Hermite filtering to obtain state estimation and covariance, and then the calculated adaptive factor can adaptively regulate the estima- tion and covariance. Thus it provides a reliable importance density function and is more suitable for filtering calcula- tion based on nonlinear and non-Gaussian models, through considering the latest measurement information and impro- ving the particle diversity precision. The proposed algorithm was applied to SINS/SAR integrated navigation system. Simulation results demonstrate that the adaptive GHPF outperforms the extended Kalman filtering, particle filtering and GHPF in terms of accuracy, thus improves the precision in navigation system.