东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (9): 1221-1224.DOI: 10.12068/j.issn.1005-3026.2016.09.002

• 信息与控制 • 上一篇    下一篇

基于严格残差选择的非视距定位算法

胡楠， 吴成东， 刘鹏达， 于晓升   

  1. (东北大学 信息科学与工程学院， 辽宁 沈阳110819)
  • 收稿日期:2015-06-17 修回日期:2015-06-17 出版日期:2016-09-15 发布日期:2016-09-18
  • 通讯作者: 胡楠
  • 作者简介:胡楠(1987-)，男，吉林梅河口人，东北大学博士研究生; 吴成东(1960-)，男，辽宁大连人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61503274，61403068)； 中央高校基本科研业务费专项资金资助项目(N140403005).

NLOS Localization Algorithm Based on the Strict Residual

HU Nan， WU Cheng-dong， LIU Peng-da， YU Xiao-sheng   

  1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China.
  • Received:2015-06-17 Revised:2015-06-17 Online:2016-09-15 Published:2016-09-18
  • Contact: HU Nan
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摘要： 无线传感器网络的移动定位近年来受到越来越多的关注.影响精确定位的一个很重要因素是非视距传播信号的存在，非视距误差使得定位精度严重下降.通过分析非视距测量值残差的特性，提出了一种严格残差选择方法来鉴别距离测量值的状态.首先利用扩展卡尔曼滤波(EKF)算法的线性回归模型获得距离测量值的残差，然后利用严格残差选择来对残差进行筛选，最后利用并行变节点EKF算法完成定位.仿真结果表明提出的算法在非视距情况下的定位效果要优于其他算法，在不同环境下该算法具有更好的鲁棒性和更高的定位精度.

关键词: 无线传感器网络, 非视距定位, 扩展卡尔曼滤波, 严格残差, 线性回归模型

Abstract: Mobile localization in wireless sensor networks (WSNs) has attracted considerable attention in recent years. One of the most important factors affecting the accuracy of localization or tracking is non-line-of-sight (NLOS) signal propagation. The NLOS error could seriously reduce the localization accuracy. By analyzing the characteristics of the residual of NLOS distance measurements， a strict residual selection method was proposed to identify the condition of the distance measurements. In this algorithm， extend Kalman filter (EKF) linear regression model was firstly utilized to get distance residuals. Then the strict residual selection was used to filtrate the residuals. Finally the localization was finished by using the parallel variable node EKF algorithm. Simulation results show that the localization of the proposed algorithm outperforms the other algorithms compared in NLOS conditions. The proposed algorithm has better robustness and higher accuracy in different environments.

Key words: wireless sensor network, non-line-of-sight(NLOS) localization, extend Kalman filter, strict residual, linear regression model

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