均方根容积代价参考粒子滤波算法

Square-Root Cubature Cost-Reference Particle Filter Algorithm

为提高复杂噪声的滤波精度,基于均方根容积卡尔曼滤波(SCKF)和代价参考粒子滤波(CRPF),提出一种新的均方根容积代价参考粒子滤波算法(SCCRPF)。算法采用SCKF和最新量测信息更新先验分布函数,生成CRPF的重要密度函数,保留了SCKF对非线性系统的滤波精度,同时获取了CRPF对噪声假设未知系统的滤波精度。仿真结果表明,对于噪声假设未知系统,SCCRPF的滤波精度高于均方根容积粒子滤波(SCPF);对于噪声假设已知系统,SCCRPF的滤波精度高于CRPF。

To improve the filtering precision under complex noise condition,a new Square-root Cubature Cost-Reference Particle Filter（SCCRPF） is proposed based on the Square-root Cubature Kalman Filter（SCKF） and the Cost-Reference Particle Filter（CRPF）.The proposed filter updates the prior distribution function with the latest measured information and SCKF,and thereby generates the importance density function for CRPF.The new filter not only reserves the precision advantage of SCKF in filtering nonlinear systems,but also possesses the filtering precision of CRPF for dealing with systems with an unknown noise assumption.Simulation results show that： the filtering precision of SCCRPF is higher than that of Square-root Cubature Particle Filter（SCPF） for a system with unknown noise assumption,and is higher than that of CRPF for a system with known noise assumption.