Dynamic System Linearization via Deep Relative Displacement Prediction for Robust IMU-WiFi Indoor Trajectory Estimation

Abstract

Standalone WiFi fingerprinting and Inertial Measurement Unit (IMU) sensors are unreliable for indoor positioning due to signal instability and cumulative drift error, respectively. We present a novel sensor fusion framework that overcomes these challenges by enhancing WiFi fingerprint quality while simplifying the fusion process. Our approach filters and ranks WiFi signals to create a stable input for a Long Short-Term Memory (LSTM), while a Convolutional Neural Network (CNN) is uniquely trained to predict relative displacement from IMU data. This latter step linearizes the system dynamics, enabling the use of a simple Linear Kalman Filter and avoiding the complexity of traditional non-linear filters. Combining these innovations, the integrated framework achieves a mean position error of 4.30 m, a 32.3% improvement over the best standalone model. This work demonstrates that our approach to WiFi selection and system linearization provides a robust and highly accurate solution for real-time indoor positioning.