IMU-assisted nearest neighbor selection for real-time WiFi fingerprinting positioning

This paper presents a nearest neighbor selection algorithm for real-time WiFi fingerprinting positioning with the assist of inertial measurement unit (IMU) measurements. The WiFi fingerprinting positioning using received signal strength (RSS) measurements suffers from the RSS variation problem. Due to this problem, reference points that are irrelevant to the user´s position are selected, and the positioning accuracy decreases. To overcome the RSS variation problem, we propose an IMU-assisted nearest neighbor selection algorithm that filters out irrelevant reference points based on the position prediction with IMU measurements. The proposed algorithm was evaluated and compared with the conventional ii-nearest neighbors (KNN) selection and the IMU-based dead-reckoning positioning in a real indoor environment. The experimental results showed that the average positioning error of the proposed algorithm was 2.41 m, whereas those of the KNN-based fingerprinting algorithm and the IMU-based dead-reckoning positioning were 3.57 m and 15.27 m.