Beom Hwang  Jaehoon Jeong

10.5626/JOK.2020.47.4.433

Indoor Positioning System Positioning scheme wireless signal intensity Beacon Particle Filter Kalman filter

This paper proposes an Indoor Positioning System to track a user’s position indoors by using beacons’ wireless signal intensity. To overcome the non-linearity of an existing indoor positioning scheme using wireless signal intensity, a particle filter is used for a positioning algorithm, so the noise of the wireless signal intensity is not directly reflected on the positioning result. In the observation phase of the particle filter, the distance from a user’s smartphone is estimated based on the wireless signal intensity, and the similarity of each particle with an estimated ground truth is calculated through the predicted distance value. Also, our proposed positioning scheme uses the random walk technique (the Monte Carlo method) to calculate a position estimation value. Additionally, to solve the well-known local minimum problem of the particle filter, the particles estimated closest to the beacons according to the distance prediction values are given proximity weights, so the particles can quickly locate the user. The positioning error on the walking path is also corrected by considering the indoor map.