联邦式扩展卡尔曼粒子滤波算法

Federal Extended Kalman Particle Filtering Algorithm

为了使联邦滤波器能有效处理非高斯、非线性系统的状态估计问题,提出将扩展卡尔曼粒子滤波引入联邦滤波结构中,得到一种新的联邦式扩展卡尔曼粒子滤波算法。使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理,从而摆脱了经典卡尔曼滤波的限制,拓宽了联邦滤波器的实际应用范围。将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验,结果表明该算法是有效性的。

A new particle filter （federal extended Kalman particle filter, EKF-FPF） was proposed to estimate the state of non- Gaussian and non-linear system for federal filter, in which extended Kalman particle filer was introduced to federal filter so that the information fusion of subsystem can be solved by the non-Gaussian and non-linear filer. By doing so, the federal filter can get rid of the disadvantage of the ordinary Kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm