Pedestrian Dead Reckoning using adaptive particle filter to human moving mode

Tracking people has become an important topic since it is needed to optimize human activities in commercial facilities, factories, and other business organizations. In buildings, where the Global Positioning System (GPS) lacks efficiency, there are mainly two positioning methods. One is called Fixed Indoor Positioning systems. The architecture is having fixed number of base stations which receives/sends the signals and calculates the coordinates of the target object. Another one is called Pedestrian Dead Reckoning (PDR). It happens when locating people who are carrying localization sensors while the building is not equipped with an indoor positioning system. The use of smart phones has rapidly spread in these days, so PDR using sensors of smart phones has been more realizable for versatile indoor tracking systems. PDR has a problem that the noise of sensors depending on sensitivity and surroundings reduces accuracy. To improve accuracy, particle filter is mostly used. Particle filter is sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. However it costs computing resource, so it consumes battery heavily in smartphones. Heavy battery consumption reduces usability and it induces that the tracking application is not used. Therefore, we developed the improved particle filter method to reduce battery consumption. The method can change the number of particles and the error distribution of sensors adaptive to human moving modes. We evaluate the entire system on a smartphone by using the sensor data of an actual person walking in a commercial facility “THE RAILWAY MUSEUM” in Omiya, Japan. Our results show that we can reduce computing time for up to one-third keeping tracking accuracy. As a result, our method can be generally used to optimize human activities using smart phones.