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.