Cubature粒子滤波

非线性非高斯下后验概率密度函数解析值无法获得，需设计合理的重要性密度函数进行逼近。传统粒子滤波(particle filter, PF)直接采用未含最新量测信息的状态转移先验分布函数作为重要性密度函数来逼近后验概率密度函数。针对PF缺乏量测信息的问题,提出一种基于Cubature卡尔曼滤波(Cubature Kalman filter, CKF)重采样的Cubature粒子滤波新算法(Cubature particle filter, CPF)。该算法在先验分布更新阶段融入了最新的观测数据，通过CKF设计重要性密度函数，使其更加接近系统状态后验概率密度。仿真表明CPF估计精度高于PF和扩展卡尔曼滤波(extended particle filter, EPF)，与无轨迹粒子滤波(unscented particle filter, UPF)相比，其精度相当，但算法运行时间降低了约20%。

Cubature particle filter

The analytical value of the posterior density function cannot be obtained in the nonlinear non-Gaussian,and needs to approximate by the exact importance density function.The traditional particle filter(PF) directly employs the state transition prior distribution function which does not include the latest measuring information as an importance density function to approximate the posterior density function.For the lack of measuring information of PF,a re-sampling Cubature particle filter(CPF) algorithm based ...