基于递归极大似然估计的几何定位评价

Ali Yassin, Youssef Jaffal, Y. Nasser
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引用次数: 2

摘要

本文提出了一种基于混合协作技术的定位算法。该算法主要分为初始定位和跟踪两大类。在通过测量接收信号强度(RSS)和到达时间(ToA)获得未定位移动终端(UMT)的距离估计后,我们通过三角测量和递归最大似然(ML)估计获得了UMT的初始位置。递归估计是通过将研究区域划分为可能位置的网格来实现的。在获得不同时间戳的初始估计测量位置后,这些位置可以通过本文提出的扩展卡尔曼滤波器(EKF)的混合组合进行增强。仿真结果表明,即使在阴影区域,该方法也具有良好的定位性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the evaluation of geometric localization using Recursive Maximum Likelihood estimation
In this paper, we propose a novel positioning algorithm based on hybrid cooperative techniques. The proposed algorithm is divided into two main categories: initial positioning and tracking. After acquiring an estimate about the distances of an un-located mobile terminal (UMT) by measuring the Received Signal Strength (RSS) and Time of Arrival (ToA), we obtain initial position for an UMT via triangulations and Recursive Maximum Likelihood (ML) estimator. The recursive estimation is achieved by dividing the studied region into a grid of possible locations. After having initial estimated measured positions at different time stamps, those positions can be enhanced via an hybrid combination through the Extended Kalman Filter (EKF) proposed in this work for tracking. Simulation results show that the proposed positioning technique performs very well, even in shadowed regions.
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