基于无迹卡尔曼滤波和权值优化的改进粒子滤波算法

An Improved Particle Filter Algorithm Based on UKF and Weight Optimization

针对传统粒子滤波面临的重要密度函数的选取和粒子多样性丧失引起的样本贫化问题,提出基于无迹卡尔曼滤波和权值优化的改进粒子滤波算法。与传统的粒子滤波算法相比,有两点改进:首先该算法采取无迹卡尔曼滤波产生建议分布函数;其次,在重采样过程,提出基于权值优化的改进重采样算法来增加粒子的多样性。仿真结果表明,改进算法降低了粒子滤波算法的粒子退化程度并避免样本贫化现象的出现,更加接近真实值,提高了跟踪精度。

Aiming at the problems of the importance function choice and the sample impoverishment after resampling,an improved particle filter algorithm was proposed in this paper,which based on the unscented Kalman filter and weight optimization.Compared with the traditional particle filter,this algorithm had two improvements,UKF was used to generate the importance density function,and weight optimization was used to ensure all useful information inherited,which could maintain the diversity of particle.The theory analysis and simulation showed that the improved particle filter algorithm could solve particle degeneracy and avoid sample impoverishment.