Robust self-localization using Wi-Fi, step/turn-detection and recursive density estimation

Indoor positioning systems are required for many new applications and, ideally, should provide high accuracy at zero costs for initial setup, maintenance and per user. Many approaches thus use an existing Wi-Fi infrastructure and smart-phones for pedestrian location estimation. While the well-known Wi-Fi fingerprinting provides good localization accuracy down to one meter, necessary time and costs are tremendous. Alternatives, like model-based signal strength estimation, are easy to setup but supply viable results only for line of sight conditions. To provide an inexpensive yet accurate solution, we combine Wi-Fi localization using a signal strength prediction model together with step/turn-detection based on a smartphone's accelerometer/gyroscope and incorporate a priori knowledge utilizing the building's floorplan. Our technique uses a statistical model for both, Wi-Fi and step/turn-detection, to calculate the probability of the pedestrian residing at some arbitrary position and leverages the building's floorplan to determine the likelihood for any possible movement between two positions. Latter probabilities are combined with the two densities from Wi-Fi and step/turn-detection applying recursive density estimation implemented using well-known particle filtering techniques. While the density estimated through Wi-Fi measurements provides a vague, absolute position at a significant uncertainty, the step-detector supplies a fine resolution at the downside of a cumulative error due to its estimation relative to previous steps. The fusion of these two sensor densities compensates for this error and the floorplan further enhances the estimation result. We will show that our statistical approach provides a robust, long-term stable location estimation, much better than Wi-Fi on its own while requiring only a few (empirical) parameters.