基于二次预测的粒子滤波算法

doi:10.13229/j.cnki.jdxbgxb201505045

摘要/Abstract

摘要： 为了解决粒子滤波在粒子采样阶段没有利用观测信息的问题,提出一种基于二次预测的粒子滤波算法(SP-PF),并在图形处理器(GPU)上进行了并行实现。首先,从状态转移函数中采样得到预测粒子,通过最小二乘估计,构建一个新的粒子采样器;然后,在预测粒子的基础上,将当前的观测信息引入到粒子的二次预测中,经过两次预测后,每个新粒子都是对当前状态的无偏估计;最后,通过粒子加权的方式对当前状态进行估计,估计完成后再对粒子进行重采样。实验结果表明:该算法的估计精度优于标准粒子滤波(SPF)、辅助粒子滤波(APF)和无迹粒子滤波(UPF),且GPU显著提升了SP-PF的处理效率。

关键词: 信息处理技术, 粒子滤波, 图形处理器, 最小二乘估计

Abstract: In order to handle the problem that current observation information is not used in the particle sampling stage of the Particle Filter (PF), a novel particle filter algorithm based on Second-Prediction (SP-PF) is proposed and implemented on the Graphics Processing Unit (GPU). First, sampling from the state transition function is performed to obtain the predicted particles, and by least square estimation, a new generator of sampling particles is constructed. Then, on the basis of the predicted particles, the current observation information is introduced into the secondary particle prediction. After double predictions, each new particle is an unbiased estimate of the current state. Finally, the current state is estimated by means of weighting these particles, and after the completion of state estimation, resampling is conducted. Experimental results demonstrate that the proposed algorithm improves the precision of estimation compared with Standard Particle Filter (SPF), Auxiliary Particle Filter (APF) and Unscented Particle Filter (UPF). Furthermore, the processing efficiency of SP-PF is greatly raised by using GPU.

Key words: information processing technology, particle filter, graphics processing unit, least square estimation

中图分类号:

TN911

武勇, 王俊, 曹运合, 张培川. 基于二次预测的粒子滤波算法[J]. 吉林大学学报(工学版), 2015, 45(5): 1696-1701.

WU Yong, WANG Jun, CAO Yun-he, ZHANG Pei-chuan. Novel particle filter algorithm based on second-prediction[J]. 吉林大学学报(工学版), 2015, 45(5): 1696-1701.

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