| 基于加权极速学习机室内高动态环境的定位算法 |
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| 引用本文： | 周世悦,张静. 基于加权极速学习机室内高动态环境的定位算法[J]. 上海师范大学学报(自然科学版), 2017, 46(2): 206-212 |
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| 作者姓名： | 周世悦 张静 |
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| 作者单位： | 上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234 |
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| 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.
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| 关 键 词： | 室内定位 高动态环境 加权极速学习机 |
| 收稿时间： | 2015-09-12 |
Indoor localization algorithm in high dynamic environment based on W-ELM |
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| Zhou Shiyue and Zhang Jing. Indoor localization algorithm in high dynamic environment based on W-ELM[J]. Journal of Shanghai Normal University(Natural Sciences), 2017, 46(2): 206-212 |
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| Authors: | Zhou Shiyue and Zhang Jing |
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| Affiliation: | College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China and College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China |
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| Abstract: | With increasing needs of people on the indoor location-based services,indoor localization research becomes more and more important.With the developing of smart city,Wi-Fi is getting more popular than before because of its moderate transmission distance.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. |
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| Keywords: | indoor localization high dynamic environment weighted extreme learning machine |
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