基于均差滤波与高斯和的非线性非高斯系统滤波算法

Nonlinear non-Gaussian system filtering based on Gaussian sum and divided difference filter

针对一类非线性非高斯系统的滤波问题,在分析均差滤波算法和高斯和滤波算法的基础上,提出一种基于均差滤波的高斯和滤波算法,适于处理非线性非高斯系统的滤波问题.对于似然密度位于条件转移概率密度拖尾处的情况,与传统的粒子滤波算法相比,所提算法能提高滤波的精度和实时性.仿真实验验证了新算法的有效性.

Based on analyzing divided difference filter（DDF） and Gaussian sum filter（GSF）, a GSF-based DDF algorithm is developed for nonlinear dynamic state space（DSS） models with non-Gaussian noise, which is suitable for the filtering problem of nonlinear/non-Gaussian systems. When the likelihood function appeares at the tall of the transfer probability density, the proposed algorithm can improve the precision of nonlinear/non-Gaussian filtering compared with the traditional particle filter（PF）. Experiments show that the proposed method works well in the filtering for DSS models with non-Gaussian noise.