A Particle Filter-Based Reinforcement Learning Approach for Reliable Wireless Indoor Positioning

Abstract

Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users' positions. In this work, we propose a particle filter-based reinforcement learning (PFRL) approach for the robust wireless indoor positioning system. Our algorithm integrates information of indoor zone prediction, inertial measurement units, wireless radio-based ranging, and floor plan into an particle filter. The zone prediction method is designed with an ensemble learning algorithm by integrating individual discriminative learning methods and Hidden Markov Models. Further, we integrate the particle filter approach with a reinforcement learning-based resampling method to provide robustness against localization failure problems such as the kidnapping robot problem. The PFRL approach is validated on a two-tier architecture, in which distributed machine learning tasks are hosted at client and edge layer. Experiment results show that our system outperforms traditional terminal-based approaches in both stability and accuracy.