面向WLAN室内定位的T检验样本容量优化方法

WLAN（Wireless Local Area Networks）室内定位已受到人们广泛的关注，而在离线指纹采集阶段常常容易造成位置指纹RSS数据采集的盲目性和不可靠性，并忽略所需采集RSS（Received Signal Strength）样本容量与定位性能的关系.为了解决这一问题，本文提出一种面向WLAN室内定位的T检验样本容量优化方法.该方法在离线阶段利用OC（Operating Characteristics）函数优化指纹数据库允许的最小RSS样本容量，而在在线阶段利用T检验方法对目标终端进行粗定位，并进而提出基于T检验的KNN（K-Nearest Neighbour）算法以完成对目标终端的精定位.此方法用有限的样本容量获得较稳定的定位性能分析结果，显著地减少了大量的人力和时间开销.

T-test Based Sample Capacity Optimization for WLAN Indoor Localization

WLAN indoor localization has caught significantly wide attention.In offline phase,the location fingerprint RSS data acquisition often results in blindness and unreliability,and ignores the relations between the required RSS sample capacity and localization performance.To solve this problem,a new T-test based sample capacity optimization approach for WLAN indoor localization is proposed.In offline phase,the Operating Characteristics (OC) function is used to optimize the allowable minimum RSS sample capacity for the fingerprint database construction.In online phase,we perform the rough localization by using the T-test approach,and then propose the T-test based KNN algorithm for the fine localization of target terminal.This method uses a limited sample capacity to obtain a more stable localization performance analysis results,significantly reducing the amount of manpower and time overhead.