An Improved PSO Approach to Indoor Localization System Based on IMU, WiFi RSS and Map Information

Abstract

Indoor localization and tracking plays a vital role in the IoT field for its various types of applications, e.g. healthcare system, pedestrian navigation, emergency rescue. Among all the available sensors, the combination of IMU and WiFi-RSS is the most widely used scheme for its low cost and pedestrian compatibility. However, conventional indoor localization systems cannot achieve satisfactory accuracy. To cope with this issue, in this paper, we propose a novel indoor localization system that integrates the IMU sensing and the RSS fingerprinting via a proposed PSO (Particle Swarm Optimization) based algorithm. We develop a new WiFi-enabled platform which can obtain RSS from captured wireless messages in the WiFi traffic. Based on this platform, the RSS fingerprints and the displacement estimated by IMU serve as the guidance and soft constraints for the particle swarm. A fitness metric is also proposed to evaluate the likelihood of each particle finding the real position by incorporating the GPR (Gaussian Process Regression) method. To improve the localization accuracy, we leverage the layout of the map, which provides information on accessible and inaccessible areas. Besides, image processing techniques including line detection and contour extraction are utilized to constrain the particles that may fall into the inaccessible areas. Experiments are conducted in a multiple function laboratory and the results verify that the proposed approach outperforms the typical approaches, and the achieved mean localization error is 0.705m.