Particle filter reinforcement via context-sensing for smartphone-based pedestrian dead reckoning

Shao W., Zhao F., Luo H., Tian H., Li J., Crivello A.

Smartphone-based navigation  Estimation  Pedestrian dead reckoning  Reinforcement learning  Electrical and Electronic Engineering  Atmospheric measurements  Computer Science Applications  Neural networks  Indoor location tracking  Particle measurements  Particle filters  Modelling and Simulation  Mathematical model  Particle filter 

Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods.

Source: IEEE communications letters (Print) 25 (2021): 3144–3148. doi:10.1109/LCOMM.2021.3090300

Publisher: Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America

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