Fusion Algorithm of WiFi and IMU for Indoor Positioning

Abstract

Due to the limitations imposed by and complexity of indoor environments, a low-cost and accurate indoor positioning system has not yet been designed. To address this issue, we constructed a fused indoor positioning algorithm based on the extended Kalman filter for WiFi and inertial measurement units (IMUs) using only a smartphone. To reduce the influence of WiFi signal fluctuation on fingerprint-based positioning, we used Gaussian process regression for denoising the data. We used our proposed improved clustering algorithm to reduce the matching amount in the positioning stage and increase the positioning accuracy. In terms of pedestrian dead reckoning (PDR) positioning, we designed a new and effective direction estimation algorithm integrating accelerometer and magnetometer, and we used an online step size estimation model to improve the accuracy of step size estimation. The experimental results showed that the average positioning error of the proposed fusion algorithm is 1.76 m, which was 55% lower than that using the WiFi network only, and 62% lower than using PDR only. Our findings showed that the fused positioning scheme based on WiFi and IMU can be used to effectively increase indoor positioning accuracy, and the proposed system is suitable for high-precision positioning scenarios.