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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 4; p. 786
• Main Authors: Zhu, Yida, Luo, Haiyong, Wang, Qu, Zhao, Fang, Ning, Bokun, Ke, Qixue, Zhang, Chen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 14.02.2019; MDPI AG
• Subjects: GNSS measurements; indoor/outdoor detection; machine learning; quickly switching; seamless indoor and outdoor navigation and positioning; smartphone; GNSS measurements; smartphone; quickly switching; seamless indoor and outdoor navigation and positioning; machine learning; indoor/outdoor detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19040786

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%.