Multilevel Magnetic Field Fingerprinting Positioning Error Elimination Method

As a commonly used indoor Internet of Things (IoT) positioning method, fingerprinting is frequently carried out by fusing inertial and magnetic data. However, magnetic signals may exhibit high similarity in extensive indoor environments, leading to increased mismatching in magnetic fingerprinting positioning results. It negatively impacts the overall accuracy of positioning outcomes, leading to inaccuracies. To address this challenge, this article presents a multilevel error elimination approach that refines magnetic positioning outcomes from coarse to fine-grained adjustments. It marks the inaugural research on error detection and elimination specifically focused on magnetic field positioning. The method employs velocity information, sliding median filtering, and neighborhood filtering to rapidly, accurately, and effectively detect and eliminate the errors of magnetic positioning results. This methodology primarily employs velocity information to rapidly and comprehensively eliminate coarse errors. Subsequently, sliding median filtering addresses scenarios where velocity-based error correction is unreliable, effectively facilitating the intermediate removal of errors in magnetic field positioning outcomes. Ultimately, neighborhood filtering addresses unreliable situations in the above processes, enabling small-scale and detailed elimination of errors in magnetic field positioning results. Within a 100 m \times 60 m indoor parking lot, the proposed approaches retained 51% to 80% of magnetic positioning results and enhanced the accuracy of magnetic positioning by over 80%.