Real-time Smartphone Indoor Tracking Using Particle Filter with Ensemble Learning Methods

Location aware services in the Internet of Things are essential for smart environments. Location awareness enables operational systems to deliver useful information for supplying context-aware applications. We propose an efficient probabilistic model to provide good and stable localization accuracy in smart building environments for smartphones. Our proposed localization method fuses zone detection, radio-based ranging, inertial measurement units and floor plan information into an enhanced particle filter. Zone detection is designed with an ensemble learning algorithm by combining Hidden Markov Models and discriminative learning methods. We first apply ensemble learning models to achieve zone detection. Further, we integrate zone detection and an enhanced ranging model to achieve high and stable localization performance. Experiment results in an office-like indoor environment show that our system outperforms traditional localization approaches considering stability and accuracy. The localization method can achieve performance with an average localization error of 1.26 meters.

[1]  Zan Li,et al.  A real-time robust indoor tracking system in smartphones , 2018, Comput. Commun..

[2]  S. Seidel,et al.  914 MHz path loss prediction models for indoor wireless communications in multifloored buildings , 1992 .

[3]  Shunsuke Kamijo,et al.  Pedestrian dead reckoning for mobile phones through walking and running mode recognition , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[4]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[5]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[6]  Moustafa Youssef,et al.  SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization , 2016, IEEE Transactions on Mobile Computing.

[7]  Johann Borenstein,et al.  Heuristic Drift Elimination for Personnel Tracking Systems , 2010, Journal of Navigation.

[8]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[9]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[10]  Zan Li,et al.  A time-based passive source localization system for narrow-band signal , 2015, 2015 IEEE International Conference on Communications (ICC).

[11]  Zan Li,et al.  A Real-time Indoor Tracking System in Smartphones , 2016, MSWiM.

[12]  Zan Li,et al.  A passive WiFi source localization system based on fine-grained power-based trilateration , 2015, 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).