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

doi:10.3390/s19040786

	A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
	Zhu, Yida 1; Luo, Haiyong 2; Wang, Qu 3; Zhao, Fang 1; Ning, Bokun 1; Ke, Qixue 1; Zhang, Chen 1
	2019-02-02
发表期刊	SENSORS
ISSN	1424-8220
卷号	19 期号:4 页码:23
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%.
关键词	machine learning quickly switching GNSS measurements indoor outdoor detection seamless indoor and outdoor navigation and positioning smartphone
DOI	10.3390/s19040786
收录类别	SCI
语种	英语
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
WOS类目	Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS记录号	WOS:000460829200037
出版者	MDPI
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4128
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 Software 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 Informat & Commun Engn, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yida,Luo, Haiyong,Wang, Qu,et al. A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning[J]. SENSORS,2019,19(4):23.
APA	Zhu, Yida.,Luo, Haiyong.,Wang, Qu.,Zhao, Fang.,Ning, Bokun.,...&Zhang, Chen.(2019).A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.SENSORS,19(4),23.
MLA	Zhu, Yida,et al."A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning".SENSORS 19.4(2019):23.