基于Student’st分布的自适应重采样粒子滤波算法  

Self-adaptive resampling particle filter based on student's t distribution

作　　者：滕飞[1] 薛磊[1] 李修和[1] 

机构地区：[1]解放军电子工程学院战役系,合肥230037

出　　处：《控制与决策》2018年第2期361-365,共5页Control and Decision

基　　金：武器装备预研重点基金项目(9140A33020112JB39085)

摘　　要：针对粒子滤波在跟踪非线性状态突变系统的隐状态时,因粒子贫化导致估计精度下降的问题,提出一种基于Student’s t分布的自适应重采样粒子滤波算法.首先,将Student’s t分布作为采样尺度转移方程,再自适应地将粒子依据权值大小分为两个子集;然后,对子集执行自适应交叉和变异操作,得到新生粒子集,从而自适应地提升粒子多样性,达到提升估计精度的目的.实验结果验证了所提出算法的可行性和有效性.For the estimation accuracy problem that a particle filter used in hidden state tracking in nonlinear state mutation system suffers from particle impoverishment, a self-adaptive resampling particle filter based on student＇s t distribution is proposed.Firstly, the algorithm employs the student＇s t distribution as the transfer function of sampling scale. Then, the particle set is divided into two subsets according to the weight. Finally, self-adaptive crossover and mutation operations are performed on the two subsets to obtain the newborn sets. This algorithm can improve the estimation accuracy by self-adaptively improving the particle diversity. The simulation results show the feasibility and effectiveness of the proposed algorithm.

关 键 词：自适应重采样粒子滤波 状态突变系统 粒子贫化 Student’s T分布 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]