Internet of things-based robust semi-analytical over ubiquitous data for indoor positioning geomagnetic

In the era of the Internet of Things (IoT), there is an increasingly urgent demand for high-precision and ubiquitous indoor positioning. However, robust technical solutions have yet to emerge, with one challenge being the effective use of geomagnetic information to address indoor personnel orientation. As known, outdoor environments with open spaces can utilize geomagnetic resolution-based methods for orientation, but indoor environments often suffer from significant magnetic distortions, rendering such methods impractical. To address this issue, this paper proposes a semi-analytical orientation method. The method initially integrates the analytical orientation results with the geometric relationship of corridor structures extracted based on spatial context information, obtaining corrected orientation results. This is achieved by measuring magnetic distortions and determining the fusion coefficient. Furthermore, the paper thoroughly analyzes the impact of different fusion coefficients on the orientation results. Test results indicate that compared to existing methods, the proposed approach exhibits better robustness, effectively improving orientation accuracy, and is widely applicable to path-based or corridor-based positioning and orientation scenarios.