LAUSR

Geomagnetic navigation techniques have become a research hotspot due to their infrastructure-free characteristic, all-weather, and all-time availability. However, challenges such as the low accuracy of mobile device sensors, the geomagnetic daily variations, and the tradeoff between positioning accuracy and time-consuming persist. To address these issues, a heuristic geomagnetic matching algorithm based on geomagnetic characteristic parameters (GCPs), which are innovatively incorporated as guiding factors into the matching process, is proposed in this article. A novel framework utilizing the capability of GCPs to represent the characteristics of matching regions is constructed. The local GCPs at both the macrolevel and microlevel are normalized to a consistent dimension, upon which the strategy is designed to select geomagnetic maps with richer information for geomagnetic matching that expands the search dimensions. Extensive experiments are conducted to evaluate the performance of the proposed algorithm. The experimental results demonstrate an average 32.61% reduction in positioning errors in simulations. In field tests, the proposed algorithm achieves error reductions of 58.87% and 34.65% compared to the pedestrian dead reckoning (PDR) algorithm and single map matching, respectively.