KF/UPF在SINS大方位失准角初始对准中的应用

Application of KF/UPF in initial alignment of large azimuth misalignment of SINS

为解决捷联惯导系统大方位失准角初始对准中状态维数较高,直接应用无迹粒子滤波(unscented particle filter,UPF)会带来维数灾难的问题,提出了基于卡尔曼滤波(Kalman filter,KF)/UPF组合滤波的初始对准方法。将非线性初始对准模型分解为线性与非线性两部分,采用KF实现对线性部分的最优估计,采用UPF对系统的非线性部分进行状态估计。通过仿真比较不同粒子数下KF/UPF组合滤波算法和UPF算法,结果表明,KF/UPF组合滤波算法在保证初始对准精度和收敛速度的同时,将需要进行UPF滤波的状态维数由10维降为3维,减少了计算量,运算时间分别缩短至原来的52.69%和6.0%,提高了初始对准的实时性。

In order to solve the problem that the error model of strapdown inertial navigation system（SINS）has high state dimension when the azimuth misalignment angle of SINS on static base is large,the method based on unscented particle filter（UPF）would bring the curse of dimensionality,a method based on the Kalman filter/unscented particle filter（KF）/UPF integrated filter is proposed.The initial alignment model is decomposed into the linear part and the nonlinear part,the optimal state estimation of the linear part is achieved by KF,and UPF is used to estimate the state value of the non-linear part.Finally,the KF/UPF algorithm is compared with the UPF algorithm in different number of particles by use of the simulation experiment,simulation results prove that not only the KF/UPF integrated filter can maintain the indexes of the high alignment accuracy and the fast convergence speed,but also the state dimension of UPF filtering is reduced from ten to three,the computation time is reduced to 52.69% and 6.0% of the reference value,respectively,which improves the real-time performance of the initial alignment.