论文部分内容阅读
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.
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 Both theoretical analysis and simulation results verify that the proposed localization scheme can significantly enhance the location precision. Moreover, the mean square (CRLB) error of position estimate approximates the CRLB when the number of reference stations increases, which indicates that the proposed ML estimator is asmptotically efficient.