中文摘要：

针对辅助粒子滤波算法计算量大,滤波效率较低的问题,提出了一种基于快速高斯变换（Fast Gaussian transform,FGT）的辅助边缘粒子滤波算法。该算法假设状态噪声是加性的,并且是高斯的,这样非线性滤波的Chapman-Kolmogorov方程的求解近似于执行了核密度估计（Kerner density estimation,KDE）,从而可将KDE中的快速算法FGT引入,以提高算法的计算效率和实时性。仿真结果表明,该算法利用少数粒子就可以获得与常规粒子滤波相似的误差,大大提高了计算效率。

英文摘要：

According to large-calculation and lower-efficiency of the auxiliary particle filter,an auxiliary marginal particle filter algorithm is proposed based on fast Gaussian transform（FGTAMPF）.Assuming that the state noise is additive and Gaussian,the solution of Chapman-Kolmogorov equation（CKE）for nonlinear filtering,is similar to executing kernel density estimation（KDE）.Then FGT of KDE is introduced to improve the calculation efficiency.The simulation results show that the calculation error obtained by the conventional particle can also be gotten by using a small number of particles,and the algorithm greatly improves the calculation efficiency.