中国邮电高校学报(英文) ›› 2009, Vol. 16 ›› Issue (5): 56-61.doi: 10.1016/S1005-8885(08)60269-1

• Networks • 上一篇    下一篇

Cooperative localization for next-generation converged
networks with closed-form solution

张一衡,崔琪楣,张平,陶小峰   

  1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2008-05-09 修回日期:1900-01-01 出版日期:2009-10-30
  • 通讯作者: 张一衡

Cooperative localization for next-generation converged
networks with closed-form solution

ZHANG Yi-heng, CUI Qi-mei, ZHANG Ping, TAO Xiao-feng   

  1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2008-05-09 Revised:1900-01-01 Online:2009-10-30
  • Contact: ZHANG Yi-heng

摘要:

In this article, a novel cooperative wireless localization scheme based on information fusion is proposed. The scheme combines large-scale arrival time and small-scale distance measurements obtained from the next-generation converged networks. The maximum likelihood (ML) estimate of the terminal’s position is derived with closed-form solution, and the Cramér-Rao lower bound (CRLB) of the estimate error is investigated. Both theoretical analysis and simulation results verify that the proposed localization scheme can significantly enhance the location precision. Moreover, the mean square error of position estimate approximates the CRLB when the number of reference stations increases, which indicates that the proposed ML estimator is asymptotically efficient.

关键词:

cooperative;localization,;time;of;arrival;(TOA),;next-generation,;converged;network,;large;scale,;small;scale

Abstract:

In this article, a novel cooperative wireless localization scheme based on information fusion is proposed. The scheme combines large-scale arrival time and small-scale distance measurements obtained from the next-generation converged networks. The maximum likelihood (ML) estimate of the terminal’s position is derived with closed-form solution, and the Cramér-Rao lower bound (CRLB) of the estimate error is investigated. Both theoretical analysis and simulation results verify that the proposed localization scheme can significantly enhance the location precision. Moreover, the mean square error of position estimate approximates the CRLB when the number of reference stations increases, which indicates that the proposed ML estimator is asymptotically efficient.

Key words:

cooperative localization;time of arrival (TOA);next-generation;converged network;large scale;small scale