Learning-Based Wifi-IMU Fusion for Unconstrained Pedestrian Indoor Localization

Indoor activities constitute a majority of a person's daily life, making location awareness essential for modern intelligent services. Widely adopted indoor localization must be seamlessly integrated into daily life. Inertial measurement unit (IMU) and WiFi received signal strength indicator (RSSI) are two ubiquitous non-intrusive sensing modalities. However, IMUs suffer from substantial cumulative errors, while RSSI measurements are inherently unstable. This paper proposes fusing IMU with RSSI. A 100Hz velocity sequence is generated from the measured 6D inertial data, serving as the input for the propagation stage in an Extended Kalman Filter (EKF) framework. The update stage occurs only when RSSI is sampled, where the observed planar position and the corresponding uncertainty are derived from transformed RSSI values from hundreds of selected and sorted access points (APs). The filtered position not only corrects the position fingerprints obtained from RSSI samples but also refines the densely sampled inertial positions. Data collected on the CUHK campus validate the effectiveness of our fusion system.