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A Hybrid Markov and LSTM Model for Indoor Location Prediction

文献类型：期刊论文

作者Wang, Peixiao2,4; Wang, Hongen1; Zhang, Hengcai3,4; Lu, Feng3,4; Wu, Sheng2,4
刊名IEEE ACCESS
出版日期2019
卷号7页码:185928-185940
关键词Indoor location prediction movement trajectory Markov-LSTM
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2961559
通讯作者Zhang, Hengcai(zhanghc@lreis.ac.cn) ; Wu, Sheng(wusheng@fzu.edu.cn)
英文摘要Accurate and robust indoor location prediction plays an important role in indoor location services. Markov chains (MCs) have been widely adopted for location prediction due to their strong interpretability. However, multi-order Markov chains (k-MCs) are not suitable for predicting long sequences due to problems of dimensionality. This study proposes a hybrid Markov model for location prediction that integrates a long short-term memory model (LSTM); this hybrid model is referred to as the Markov-LSTM. First, a multi-step Markov transition matrix is defined to decompose the k-MC into multiple first-order MCs. The LSTM is then introduced to combine multiple first-order MCs to improve prediction performance. Extensive experiments are conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.
WOS关键词PEOPLE MOVEMENT ; VIEW
资助项目National Natural Science Foundation of China[41771436] ; National Natural Science Foundation of China[41701521] ; National Key Research and Development Program of China[2016YFB0502104] ; National Key Research and Development Program of China[2017YFB0503500] ; Digital Fujian Program[2016-23]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000510024300016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Digital Fujian Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/132156]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Hengcai; Wu, Sheng
作者单位1.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China
2.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, IGSNRR, Beijing 100101, Peoples R China
4.Fuzhou Univ, Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350002, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Peixiao,Wang, Hongen,Zhang, Hengcai,et al. A Hybrid Markov and LSTM Model for Indoor Location Prediction[J]. IEEE ACCESS,2019,7:185928-185940.
APA Wang, Peixiao,Wang, Hongen,Zhang, Hengcai,Lu, Feng,&Wu, Sheng.(2019).A Hybrid Markov and LSTM Model for Indoor Location Prediction.IEEE ACCESS,7,185928-185940.
MLA Wang, Peixiao,et al."A Hybrid Markov and LSTM Model for Indoor Location Prediction".IEEE ACCESS 7(2019):185928-185940.

入库方式： OAI收割

来源：地理科学与资源研究所

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