Research on the key technologies of indoor location cloud platform
(2.Shandong University of Science and Technology, Qingdao, Shandong Province, China 266590)
【Abstract】Aiming at the common problems that indoor positioning is discontinuous and unavailable, this paper built a private cloud platform of location service and realized the key technology of indoor hybrid positioning. The research utilized hardware resource virtualization to implement dynamic deployment, elastic computing, and on-demand cloud computing services. A hybrid cloud positioning technique for beacon node correction and autonomous estimation of trajectories was proposed and improved the continuity and usability of indoor positioning with positioning accuracy of approximately 2 m. The research also utilized the microservices management scheduling technology and its cloud-push-service component to solve the bottleneck about the online service and data communication for large scale users. The public cloud software for location service and terminal application was developed. They integrated indoor map and hybrid cloud positioning service and realized the cloud service of indoor location.
【Keywords】 cloud platform; indoor hybrid positioning; beacon node correction; autonomous estimation of trajectories; location service;
 WEISER M. The computer for the 21st century [J]. Scientific American, 1991, 65 (3): 94–104.
 CHEN Ruizhi, CHEN Liang. Indoor positioning with smartphones: the state-of-the-art and the challenges [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46 (10): 1316–1326 (in Chinese).
 WANG Yijian. Research and implementation of key technologies for bluetooth indoor positioning [D]. Nanjing: Southeast University, 2015 (in Chinese).
 Apple Company. Getting started with iBeacon version 1.0[M]. [S. l.]: [s. n.], 2014.
 LIU Keqiang. Study on human activity recognition method based on indoor location and multiple contexts [D]. Xuzhou: China University for Mining & Technology, 2017 (in Chinese).
 LI Xin, WANG Jian, LIU Chunyan, et al. Integrated WiFi/PDR/Smartphone using an adaptive system noise extended Kalman filter algorithm for indoor localization [J]. ISPRS International Journal of Geo-Information, 2016, 5 (2): 8.
 JEON J S, KONG Y, NAM Y, et al. An indoor positioning system using bluetooth RSSI with an accelerometer and a barometer on a smartphone [C] ∥Proceedings of the 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). Krakow, Poland: IEEE, 2015: 528–531.
 CHENGYaqing. A security system test environment deployment plan based on private cloud platform [J]. Police Technology, 2019 (1): 83–86 (in Chinese).
 GUANWenchao. Research on the construction of smart fire cloud platform based on Docker and its typical applications [D]. Shanghai: East China University of Science and Technology, 2018 (in Chinese).
 LI Suxuan. Research on SaaS application construction method based on microservices architecture [D]. Guangzhou: South China University of Technology, 2016 (in Chinese).
 CHEN R, PEL L, CHEN Y. A smart phone based PDR solution for indoor navigation [C] ∥Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institude of Navigation. Portland: ION, 2011.
 ZHENG Kai. Research and implementation of indoor positioning method based on pedestrian dead reckoning [D]. Nanjing: Southeast University, 2017 (in Chinese).
 FENG Kun. Research and implementation of EKF indoor positioning system based on fusing PDR/BLE [D]. Xuzhou: China University of Mining and Technology, 2018 (in Chinese).
 NIU Huan, LIAN Baowang. An integrated positioning method for GPS + PDR based on improved UKF filtering [J]. Bulletin of Surveying and Mapping, 2017 (7): 5–9 (in Chinese).
 CHEN Ruizhi, CHEN Wei, CHEN Xiang, et al. Sensing strides using EMG signal for pedestrian navigation [J]. GPS Solutions, 2011, 15 (2): 161–170.