电子学报 ›› 2020, Vol. 48 ›› Issue (8): 1572-1579.DOI: 10.3969/j.issn.0372-2112.2020.08.016

• 学术论文 • 上一篇    下一篇

基于Wi-Fi多维参数特征的无源目标跟踪技术

田增山, 廉颖慧, 周牧, 李泽, 金悦   

  1. 重庆邮电大学通信与信息工程学院, 重庆 40065
  • 收稿日期:2019-11-07 修回日期:2020-03-19 出版日期:2020-08-25 发布日期:2020-08-25
  • 通讯作者: 廉颖慧
  • 作者简介:田增山 男,1968年生于河南固始.现为重庆邮电大学教授、博士生导师.主要研究方向为蜂窝网无线定位系统、个人通信、GPS 精密定位和姿态测量、数据压缩和数据融合. E-mail:tianzs@cqupt.edu.cn
  • 基金资助:
    国家自然科学基金(No.61901076,No.61771083);重庆市教育委员会科学技术研究计划青年项目资助项目(No.KJQN201900603);重庆市基础科学与前沿研究计划项目(No.cstc2015jcyjBX0065)

Passive Target Tracking Technology Based on Wi-Fi Multi-Dimensional Parameter Feature

TIAN Zeng-shan, LIAN Ying-hui, ZHOU Mu, LI Ze, JIN Yue   

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2019-11-07 Revised:2020-03-19 Online:2020-08-25 Published:2020-08-25

摘要: 针对Wi-Fi无源目标跟踪技术中,由于直射路径信号以及噪声等影响造成提取目标反射路径信号困难等难点,本文提出了基于Wi-Fi多维参数特征的无源目标跟踪技术.该技术采用串行干扰消除代替全零初始化来完成某时刻多条路径到达角(Arrival of Angle,AoA)、飞行时间(Time of Flight,ToF)以及多普勒频移(Doppler Frequency Shifts,DFS)的初始化,并且对传统频域空间交替广义期望最大化(Frequency Domain Space Alternating Generalized Expectation-maximization,FD-SAGE)算法进行改进,弥补了传统算法收敛速度慢以及噪声影响等缺陷.除此之外,本文采用基于最小代价多路径网络的混合数据关联方法解决了在不同时刻具有不同路径数目时无法进行路径有效关联的问题,同时该方法将固定时间窗中的最优匹配作为某时刻的关联数据,避免了某次关联错误导致后续关联失败所造成的不可逆错误.实验结果表明,本文在复杂室内环境下能够达到1.3m的平均跟踪定位精度.

关键词: 信道状态信息, 多维参数联合估计, 混合数据关联, 无源目标跟踪

Abstract: In the Wi-Fi passive target tracking technology,it is difficult to extract the target's reflected path signals due to direct path signals and noise effects.A passive target tracking technology based on Wi-Fi multi-dimensional parameter feature is proposed.This technology uses serial interference cancellation instead of all-zero initialization to complete multiple paths initialization including Arrival of Angle(AoA),Time of Flight(ToF)and Doppler Frequency Shifts(DFS).And the Frequency Domain Space Alternating Generalized Expectation-maximization(FD-SAGE)algorithm is improved to make up for traditional algorithms shortcomings such as slow convergence speed and noise impact.In addition,a hybrid data association method based on the least-cost multi-path network is used to solve the problem of unable to effectively associate paths when there are different numbers of paths at different times.Meanwhile,the method uses the optimal match in a fixed time window as the correlation data at a certain time,which reduces the irreversible error caused by a subsequent association failure caused by the association error.Experimental results show that this paper can achieve an average tracking accuracy of 1.3m in a complex indoor environment.

Key words: channel state information, joint estimation of multidimensional parameters, hybrid data association, passive target tracking

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