An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9032
• Main Authors: Li, Jie, Sun, Liming, Liu, Dongpeng, Yu, Ruiyun, Wang, Xingwei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22-11-2022; MDPI
• Subjects: Accuracy; Algorithms; Cold; Fingerprints; Geomagnetism; Global positioning systems; GPS; Indoor environments; indoor localization; Infrastructure; Internet of Things; Kalman filter; Localization; Location based services; Magnetic fields; Measuring instruments; Methods; multisensor fusion; Navigation systems; PDR; Radio frequency identification; Sensors; Technology; Uncertainty; Walking; Wireless access points; Wireless local area networks (Computer networks); multisensor fusion; magnetic fields; sensors; Kalman filter; edge computing; indoor localization; PDR
• DOI: 10.3390/s22239032
• EISSN: 1424-8220

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs).