Indoor Localization in Multi-Path Environment based on AoA with Particle Filter

Filter (PF) is a promising technique for indoor location estimation and tracking. In an indoor environment, localization has become significantly challenging due to multipath reflections. This work addresses the problem of indoor localization of a Moving Target (MT) in a rich multipath environment by fusing acceleration data obtained from Inertial Measurement Unit (IMU) sensors and Angle of Arrival (AoA) measurements. First, the moving target position is predicted using the IMU sensor data. Thereafter, MUltiple SIgnal Classification (MUSIC) algorithm is applied to estimate the AoA of the multipath components. IMU sensor data and the estimated AoA of the multipath components are then fused using the probabilistic framework of the PF to estimate the moving target location. Simulation results demonstrate that the proposed system can achieve a location accuracy of less than $2m$ in a rich multipath environment with only 2 WiFi Access Points (APs).