改进高阶容积粒子滤波的系统状态估计算法

传统粒子滤波（PF）直接采用状态转移先验分布作为重要性密度函数来近似后验概率密度函数，使得后验概率密度函数未包含量测信息。针对此问题，提出了一种改进高阶容积粒子滤波(CPF)的系统状态估计算法。算法采用七阶正交容积卡尔曼滤波（7th-CQKF）对PF的粒子进行传递，使得先验分布更新阶段融入最新量测信息；通过7th-CQKF设计重要性密度函数，提高对状态后验概率密度的逼近程度；通过反比例函数计算粒子权重，突出大噪声粒子与小噪声粒子权重差别，提高粒子有效性。仿真结果表明，改进高阶容积粒子滤波的估计精度高于容积粒子滤波（CPF）。

An improved high-degree cubature particle filter system state estimation algorithm

The traditional particle filter(PF) directly employs the state transition prior distribution as an importance density function to approximate the posterior density function,which makes the posterior density function does not include the latest measurement information.For this problem,an improved high-degree cubature particle filter(CPF) system state estimation algorithm is proposed.The algorithm makes use of seventh-degree Cubature Quadrature Kalman Filter(7th-CQKF) to transmit the particles of PF,and incorporates the latest measurement information into the prior updating phase.The approximating precision of state posterior density function is improved through designing importance density function with 7th-CQKF.The algorithm calculates the weights of particles by inverse proportional function to lay emphasis on the weight difference between large-noise particles and little-noise particles,and enhances the effectiveness of particles.Simulation results show that the proposed algorithm has higher state estimation precision than CPF.