摘要
结合粒子滤波和Kalman滤波的边缘粒子滤波(MPF)是一种新的混合线性/非线性系统的状态估计方法,但是粒子滤波在计算上的复杂使得MPF难以兼顾系统实时性和精度的要求。针对此问题,提出一种基于相似采样粒子滤波算法的MPF滤波框架。算法从系统观测值中采样粒子,并通过一个计算相邻时刻粒子转移概率的步骤,提高了粒子使用率,使得算法能以少量粒子实现对非线性状态量的估计,进而提高Kalman滤波的精度和实时性。给出了算法原理分析和实现流程。以混合坐标系下的目标跟踪为对象,利用蒙特卡罗仿真研究了ILLH_MPF算法的应用,并与常规MPF方法进行了对比。
The Marginalized Particle Filter（MPF）,which combines Particle Filter（PF） with Kalman filter,is an effective algorithm for the estimation of mixed linear/nonlinear system,but the computational complexity of PF make it difficult to meet the requirement of real-time and precision in estimation.A likelihood-sampling PF was introduced to join in the MPF algorithm.The ILLH_MPF algorithm sampled the particles from the observation,and increased the utilization factor of particles by calculating the transition probability of adjacent time＇s particles. Compared with MPF, this algorithm was expected to get a good estimation precision with fewer particles, and could improve the Kalman filter＇s precision and efficiency. The principle and detailed procedure of this algorithm was introduced. The Monte-Carlo simulation was designed to show the ILLH_MPF algorithm＇s application for a problem of target-tracking in the hybrid coordinate.
出处
《电光与控制》
北大核心
2009年第11期55-59,共5页
Electronics Optics & Control
关键词
混合线性/非线性
粒子滤波
相似性采样
MPF滤波
蒙特卡罗仿真
mixed linear/nonlinear
Particle Filter
likelihood-sampling
Marginalized Particle Filter
Monte-Carlo simulation
