自适应平方根Unscented粒子滤波算法研究

Proposing Adaptive Square Root Unscented Particle Filtering(ASRUPF) Algorithm

针对粒子滤波存在的重要性密度函数难以选取和可能出现粒子退化的问题,在吸收平方根滤波、自适应滤波和粒子滤波优点的基础上,提出了一种新的UPF算法。该算法由UKF算法得到重要性密度函数,通过自适应因子实时控制动力学模型误差,采用平方根分解法抑制系统状态协方差矩阵的负定性。仿真结果证明,文中所提出的自适应平方根UPF算法,不但适用于非线性、非高斯动态系统的滤波计算,而且能有效地改善滤波性能,提高SINS/SAR组合导航系统的定位精度。

Aim. The introduction of the full paper reviews a number of papers in the open literature, points out what we believe to be their shortcomings, and then proposes our ASRUPF algorithm, which is explained in section 1. Its core consists of： （ 1 ） we obtain the importance density function with the unscented Kalman filtering （UKF） algorithm; （2） we correct the system dynamic error in real time by using the adaptive factor; （3） we enhance the numerical stability of state covariance matrix with the square root decomposition method. Section 2 simulates our ASRUPF algorithm by applying the unscented particle filtering （UPF） algorithm and our ASRUPF algorithm respectively to a strapdown inertial navigation system/synthetic aperture radar （SINS/SAR） integrated navigation system; the simulation results, given in Figs. 1 and 2, and their comparison plus other analysis demonstrate preliminarily that our ASRUPF algorithm is suitable for non-linear and non-Gaussian system filtering and can effectively enhance the positioning accuracy of the SINS/SAR integrated navigation system.