Heterogeneous Feature Machine Learning for Performance-enhancing Indoor Localization

被引：0
|
作者：
Zhang, Lingwen [1 ]
Xiao, Ning [1 ]
Li, Jun [2 ]
Yang, Wenkao [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Comp Engn, Beijing, Peoples R China
[2] NYU, Tandon Sch Engn, New York, NY USA
来源：
2018 IEEE 87TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING) | 2018年
关键词：
Indoor localization; heterogeneous features fusion (HFF); machine learning;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Currently there is a trend in indoor localization by utilizing machine learning. However, the precision and robustness are limited due to single feature machine learning scheme. The reason behind is that single feature cannot capture the complete channel characteristics and susceptible to interference. The objective of this paper is to introduce heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Its effectiveness and efficiency are proved by comparing with current benchmark.
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页数：5
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