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中文摘要：
介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念，说明了重要性函数对于粒子滤波器的设计是至关重要的。随后，给出了一种将EKF算法作为重要性函数的EPF算法，并提出将其用于静基座条件下的惯导系统非线性初始对准，通过计算机仿真对比了EPF和EKF的估计效果。仿真结果表明，EPF算法较传统的EKF算法对准时间更快，对准精度更高。
英文摘要：
The principle of Recursive Bayesian estimation was introduced which was the basis of Particle filter, and the significance of importance function to the design of particle filter was illustrated. An extended particle filter（EPF） algorithm was given whose importance function is extended Kalman filter（EKF）. The EPF was used to estimate the INS alignment on stationary base, and the simulation result was compared with that of EKF. The simulation results show that the EPF is shorter in alignment time and more accurate in estimation precision than that of EKF in the case of low uncertainties in heading and tilt angle.
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