电子学报 ›› 2019, Vol. 47 ›› Issue (1): 197-203.DOI: 10.3969/j.issn.0372-2112.2019.01.026

• 学术论文 • 上一篇    下一篇

基于粒子滤波和交互多模型的移动定位方法

夏楠, 王珏, 李博   

  1. 大连工业大学信息科学与工程学院, 辽宁大连 116034
  • 收稿日期:2017-12-18 修回日期:2018-03-12 出版日期:2019-01-25 发布日期:2019-01-25
  • 通讯作者: 夏楠
  • 作者简介:王珏 女,1986年11月生于黑龙江齐齐哈尔.现为大连工业大学信息科学与工程学院讲师.主要研究方向为智能信息处理技术.E-mail:ragnarok1876@163.com;李博 男,1986年2月生于黑龙江哈尔滨.现为大连工业大学信息科学与工程学院讲师.主要研究方向为智能信息处理技术.E-mail:libo_219vip@163.com
  • 基金资助:
    辽宁省博士启动基金(No.201501191);辽宁省自然科学基金计划重点项目(No.20170520305)

A Mobile Localization Method Based on Particle Filter and Interacting Multiple Models

XIA Nan, WANG Jue, LI Bo   

  1. College of Information Science and Engineering, Dalian Polytechnic University, Dalian, Liaoning 116034, China
  • Received:2017-12-18 Revised:2018-03-12 Online:2019-01-25 Published:2019-01-25

摘要: 为提高非视距情况下移动辐射源的定位精度,提出一种改进的交互粒子滤波算法.该算法对目标运动多模型和信号到达时间差测量噪声分布多模型联合建模.在交互多模型状态更新中利用粒子滤波对目标时变状态以及视距/非视距混合信道参数进行估计,抑制了非视距测量误差对移动定位的影响.仿真结果表明,改进算法的性能要优于现有的视距条件运动多模型和视距/非视距条件单一运动模型的定位算法,并且定位误差接近于推导的后验克拉美劳下界.

关键词: 移动定位, 视距, 非视距, 粒子滤波, 交互多模型, 到达时间差, 后验克拉美劳下界

Abstract: To improve the positioning accuracy of a mobile transmitter under the non-line-of-sight (NLOS) condition,an enhanced interacting particle filtering algorithm is proposed.The multiple motion models of the target and the multiple measurement noise distribution models of the time difference of arrival (TDOA) of the target signal are jointly built.In the state update phase of the interacting multiple models,the particle filtering is utilized to estimate the time-varying state of the target and the line-of-sight (LOS)/NLOS mixed channel parameters,thus the effect of NLOS measurement errors on mobile localization can be eliminated.Simulation results demonstrate that the proposed method performs better than the existing multiple motion model positioning method under the LOS condition and single motion model positioning method under the LOS/NLOS condition,and is close to the derived posterior Cramer-Rao lower bound.

Key words: mobile localization, line-of-sight (LOS), non-line-of-sight (NLOS), particle filter, interacting multiple models, time difference of arrival (TDOA), posterior Cramer-Rao lower bound (PCRLB)

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