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-gene...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 Cramer-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.展开更多
基金supported by the Program for New Century Excellent Talents in University (NCET-05-0116)the Hi-Tech Research and Development Program of China (2006AA01Z283, 2007AA01Z261)+1 种基金the National Natural Science Foundation of China (60702051)the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP-20070013028)
文摘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 Cramer-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.
