Research on multi-sensor assisted WiFi signal fingerprint indoor location method based on extended Kalman filter

Abstract

In the field of the mobile robot indoor location, aiming at the problems of poor stability of the WiFi signal fingerprint location and low accuracy of the single sensor location, this paper proposes a multi-sensor fusion assisted WiFi signal fingerprint location method for a mobile robot. This method is based on the extended Kalman filter (EKF) algorithm, combined with the trajectory information obtained from the inertial measurement unit (IMU) and the odometer, to fuse and correct the WiFi signal fingerprint positioning results, so as to realize a fusion positioning method with WiFi positioning as the main and multi-sensor positioning as the auxiliary. The experimental results show that the average positioning error of the fusion positioning algorithm proposed in this paper is controlled at 0.98 m, which can effectively solve the problem that fingerprint positioning using WiFi signal is greatly disturbed by the environment, and avoid the cumulative error caused by dead reckoning (DR), and improve the robustness and positioning accuracy of the positioning system.