计算机应用 ›› 2013, Vol. 33 ›› Issue (02): 595-599.DOI: 10.3724/SP.J.1087.2013.00595

• 典型应用 • 上一篇    下一篇

基于不同重采样算法的RFID指纹定位

黄保虎1,刘冉2,张华1,张昭1   

  1. 1. 西南科技大学 机器人技术及应用四川省高等学校重点实验室，四川 绵阳621010
    2. 图宾根大学 认知系，德国 图宾根 72076
  • 收稿日期:2012-08-15 修回日期:2012-10-01 发布日期:2013-02-25 出版日期:2013-02-01
  • 通讯作者: 黄保虎
  • 作者简介:黄保虎(1986-)，男，河南信阳人，硕士研究生，主要研究方向：RFID室内机器人定位;
    刘冉(1986-)，男，安徽淮北人，博士研究生，主要研究方向：RFID导航、计算机视觉;
    张华(1969-)，男，四川绵阳人，教授，主要研究方向：智能机器人、智能控制;
    张昭(1986-)，男，河南开封人，硕士研究生，主要研究方向：无芯片RFID标签结构设计。
  • 基金资助:
    绵阳市科技计划项目

RFID fingerprint-based localization based on different resampling algorithms

HUANG Baohu1,LIU Ran2,ZHANG Hua1,ZHANG Zhao1   

  1. 1. Sichuan Province Key Laboratory of Robot Technology and Application, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
    2. Department of Cognitive Systems, University of Tübingen, 72076 Tübingen, Germany
  • Received:2012-08-15 Revised:2012-10-01 Online:2013-02-25 Published:2013-02-01
  • Contact: HUANG Baohu

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摘要： 为满足移动机器人精确定位的需求，提出一种基于不同重采样算法的粒子滤波指纹定位法。定位阶段首先利用机器人运动学建立运动模型作为粒子预测分布, 并将当前的观测信息和环境指纹融入, 以改善滤波效果, 减少所需粒子数;然后给出精致重采样(ER)算法，以提高粒子的细化能力，减少粒子匮乏效应并提高定位精度;最后分析不同重采样算法对定位精度的影响，且从不同的实验角度进一步验证定位算法的精确性以及可靠性。实验结果表明, 该算法在定位精度和鲁棒性方面都有显著提高。

关键词: 粒子滤波, 指纹, 相似性度量, 重采样算法, K近邻

Abstract: In order to meet the needs of precise positioning of the mobile robot, a fingerprint positioning method of particle filter based on different resampling algorithms was presented. Firstly, during the positioning phase, the motion model built on robot kinematics served as the proposal density of particle filter, and the observation information and environment fingerprint were infused into the filtering process to enhance the particles' refining capacity and reduce the required number of particles. Secondly, an Exquisite Resampling (ER) algorithm was introduced to improve the refining ability of the particles, thus the effect of particle impoverishment could be decreased and the localization accuracy could be improved. At last, the influence of the positioning accuracy caused by different re-sampling algorithms was analyzed, and a further investigation on the accuracy and reliability of localization algorithm from different experimental perspectives was given. The experimental results show that this algorithm has the advantages in localization accuracy and robustness.

Key words: particle filter, fingerprint, similarity measure, resampling algorithm, K-nearest neighbors

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