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针对标准粒子滤波算法中存在的重要性密度函数难以选取的问题,提出了一种新的迭代平方根容积粒子滤波(ISCPF)算法.将高斯-牛顿迭代和平方根容积卡尔曼滤波(SCKF)算法相结合,得到迭代平方根容积卡尔曼滤波(ISCKF)算法.利用ISCKF算法获得粒子滤波算法的重要性密度函数,有效抑制了粒子退化现象.捷联惯导系统大方位失准角初始对准的仿真结果表明:该算法对航向失准角的估计精度可以达到2.21′,相比于标准粒子滤波(PF)算法和容积粒子滤波算法(CPF)具有更高的估计精度.
Aiming at the problem that the importance density function exists in the standard particle filter algorithm is difficult to choose, a new iterative square root volume particle filter (ISCPF) algorithm is proposed. Combining the Gauss-Newton iterative method and the square-root volumetric Kalman filter (SCKF) (ISCKF) algorithm was obtained.The ISCKF algorithm was used to obtain the importance density function of the particle filter algorithm, which effectively suppressed the particle degeneration phenomenon.The simulation results of the initial alignment of the SINS misorientation : The proposed algorithm can achieve an estimated accuracy of 2.21 ’for the heading misalignment angle and has higher estimation accuracy than the standard particle filter (PF) algorithm and volumetric particle filter algorithm (CPF).