基于加权极速学习机室内高动态环境的定位算法  

Indoor localization algorithm in high dynamic environment based on W-ELM

作　　者：周世悦 张静[1] 

机构地区：[1]上海师范大学信息与机电工程学院,上海200234

出　　处：《上海师范大学学报（自然科学版）》2017年第2期206-212,共7页Journal of Shanghai Normal University(Natural Sciences)

摘　　要：随着人们对室内基于位置服务的需求越来越大,室内定位的研究变得越来越重要.Wi-Fi由于其传输距离适中,在智慧城市发展的推动下,热点的覆盖也非常多.因此基于Wi-Fi的定位技术成为众多室内定位技术中最具有可行性的.面对室内无线环境高动态变化的情况,提出了基于加权极速学习机(W-ELM)的定位方法,实验证明该方法能够有效提高定位精度.With increasing needs of people on the indoor location-based services,indoor localization research becomes more andmore important. With the developing of smart city,Wi-Fi is getting more popular than before because of its moderate transmissiondistance. Thus, Wi-Fi based location method is the most feasible technology among many other types of indoor location methods. For the problem of signal changes dynamically in indoor environment, we proposed a weighted extreme learning machine （ W-ELM ） -based indoor localization algorithm to build a stable model, and experiment results show that this method can effectively improve the positioning accuracy.

关 键 词：室内定位 高动态环境 加权极速学习机 

分 类 号：TN929.5[电子电信—通信与信息系统]