论文部分内容阅读
基于位置的服务(Location Based Service,LBS)逐渐成为蜂窝网提供给移动用户必不可少的服务之一,而如何能够快速、准确、高效地获取移动终端的位置信息变得日益迫切.本文通过考察一种曾用于无线传感器网络(Wireless Sensor Network,WSN)节点的定位算法——Monte Carlo Localization(MCL)算法,将其移植到蜂窝网中用于移动终端节点在二维平面内的定位.此外,为了解决MCL算法计算量大而导致的计算能耗高这一问题,又引入了捕食搜索策略对MCL算法进行改进.文中对蜂窝网移动终端定位环境进行了仿真实验,分别对算法的收敛性、定位精度、位置预测样本数、定位时间、移动终端的移动速度和基站数量进行了测评并与其它定位算法进行了对比.实验结果表明,改进的MCL算法不仅在定位精度上优于三种TDOA算法(Fang,Taylor,Friedlander),而且在定位计算量上明显低于原始MCL算法.由此可得出在权衡定位精度和定位计算量的条件下改进的MCL算法优于其他三种TDOA算法(Fang,Taylor,Friedlander)及原始MCL算法的结论.
Location Based Service (LBS) has gradually become one of the essential services provided by cellular networks to mobile users, and how to obtain the location information of mobile terminals quickly, accurately and efficiently becomes more and more urgent.Through investigation A locating algorithm used in wireless sensor network (WSN) nodes, the Monte Carlo Localization (MCL) algorithm, is ported to the cellular network for location of the mobile terminal node in a two-dimensional plane. , In order to solve the problem of high computational energy consumption caused by large computational load of MCL algorithm, a predator-prey search strategy is introduced to improve the MCL algorithm.In this paper, a simulation experiment of mobile terminal location environment in cellular network is carried out, and the convergence of the algorithm , Positioning accuracy, position prediction sample number, positioning time, mobile terminal moving speed and base station number were compared and compared with other positioning algorithms.The experimental results show that the improved MCL algorithm not only outperforms the three TDOA Algorithm (Fang, Taylor, Friedlander), but also in the positioning calculation is significantly lower than the original MCL algorithm. It can be drawn in The improved MCL algorithm is superior to the other three TDOA algorithms (Fang, Taylor, Friedlander) and the original MCL algorithm to balance the positioning accuracy and the location computation.