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A VLC and IMU integration indoor positioning algorithm with weighted unscented Kalman filter

In this paper, an indoor positioning algorithm based on visible light communication (VLC), combined with inertial measurement unit (IMU) is proposed to achieve the target of high-precision positioning in the indoor environment. A linearization method is proposed to estimate the quaternion errors and velocity errors with adaptive Kalman filtering (AKF). Then, the tilt angle is adopted in VLC received signal strength indicator (RSSI) propagation model to reduce the positioning errors, which is provided by IMU data. Furthermore, an indoor positioning algorithm based on VLC and IMU integration using weighted unscented Kalman filter (WUKF) is developed to obtain the final positioning solutions. Simulation results show that the proposed VLC and IMU integration algorithm can reduce the positioning solution errors to less than 0.23 m.