Aiming at the nonlinear filtering problem, this paper proposes an improved adaptive Sage-Husa particle filtering algorithm by using Sage-Husa filtering to obtain state estimation and covariance, thus it provides a reliable importance density function that considers the latest measurement information. Then the Euclidean distance and accuracy factor constructed from statistic performance of measurement information can adaptively regulate the weight function. Thus it is more suitable for filtering calculation based on nonlinear and non-Gaussian models, through preventing the particles from degeneracy and increasing the precision of filtering. By applying the proposed algorithm into SINS/SAR integrated navigation system and comparing with extended Kalman filtering and particle filtering, the experiments demonstrate that east and north position error of adaptive Sage-Husa particle filtering are within 5.3 m± respectively, and it outperforms the extended Kalman filtering and particle filtering in terms of accuracy, thus improving the calculation precision in navigation system.% 针对非线性滤波问题，提出一种新的自适应Sage-Husa粒子滤波算法.通过Sage-Husa滤波方法计算状态估值和协方差阵来获得重要性密度分布函数，充分考虑了最新量测信息的影响，并利用欧氏距离和反映量测噪声统计特性的精度因子自适应地调整粒子权值的分布，降低粒子退化程度，提高了滤波精度，适用于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，与扩展 Kalman 滤波和粒子滤波比较，仿真结果表明，自适应 Sage-Husa 粒子滤波能提高导航系统定位的解算精度，得到的东向和北向定位误差控制在5.3 m±附近，其性能明显优于扩展 Kalman滤波和粒子滤波.
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