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A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

文献类型：期刊论文

作者Luo, Haiyong2; Zhu, Yida3; Zhang, Chen3; Ke, Qixue3; Ning, Bokun3; Zhao, Fang3; Wang, Qu1
刊名SENSORS
出版日期2019-02-02
卷号19期号:4页码:23
ISSN号1424-8220
关键词machine learning quickly switching GNSS measurements indoor outdoor detection seamless indoor and outdoor navigation and positioning smartphone
DOI10.3390/s19040786
英文摘要The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.
资助项目National Key Research and Development Program[2018YFB0505200] ; National Natural Science Foundation of China[61872046] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向Chemistry ; Electrochemistry ; Instruments & Instrumentation
语种英语
出版者MDPI
WOS记录号WOS:000460829200037
源URL[http://119.78.100.204/handle/2XEOYT63/4127]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Luo, Haiyong; Zhao, Fang
作者单位1.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China
3.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Haiyong,Zhu, Yida,Zhang, Chen,et al. A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning[J]. SENSORS,2019,19(4):23.
APA Luo, Haiyong.,Zhu, Yida.,Zhang, Chen.,Ke, Qixue.,Ning, Bokun.,...&Wang, Qu.(2019).A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.SENSORS,19(4),23.
MLA Luo, Haiyong,et al."A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning".SENSORS 19.4(2019):23.

入库方式： OAI收割

来源：计算技术研究所

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