区间衍生粒子滤波器

针对非线性、非高斯环境下具有不确定动态模型参数的系统状态估计问题,提出了一种新颖的区间衍生粒子滤波算法.该算法利用区间滤波生成的重要性密度函数,在系统状态转移概率密度的基础上,融入最新的系统观测数据,从而提高了对系统状态后验概率的逼近程度.为了进一步提高算法的实时性,提出一种类似光子衍射的粒子衍生过程,进而缓解了滤波精度与运算量之间的矛盾.通过陀螺/星敏感器组合定姿问题验证了该算法的有效性和鲁棒性.

Interval diffracted particle filter

A new particle filter based on interval filter and particle diffraction is proposed for the on-line estimation of non-Gaussian and nonlinear system with uncertain dynamics modeling. This algorithm computes the more accurate importance density function, which integrates the latest observations into the system state transition density, so that the approximation to the system posterior density is improved. At the same time, the workload of calculation is reduced by treating particle diffraction like light diffraction. A simulation experiment on the SINS/CNS (strap-down inertial navigation system/celestial navigation system) attitude estimation shows the effectiveness and robustness of the improved algorithm.