Multi-geomagnetic-component assisted localization algorithm for hypersonic vehicles

Owing to the lack of information about geomagnetic anomaly fields, conventional geomagnetic matching algorithms in near space are prone to divergence. Therefore, geomagnetic matching navigation algorithms for hypersonic vehicles are also prone to divergence or mismatch. To address this problem, we propose a multi-geomagnetic-component assisted localization (MCAL) algorithm to improve positioning accuracy using only the information of the main geomagnetic field. First, the main components of the geomagnetic field and a mathematical representation of the Earth's geomagnetic field (World Magnetic Model 2015) are introduced. The mathematical relationships between the geomagnetic components are given, and the source of geomagnetic matching error is explained. We then propose the MCAL algorithm. The algorithm uses the intersections of the isopleths of the geomagnetic components and a decision method to estimate the real position of a carrier with high positioning accuracy. Finally, inertial/geomagnetic integrated navigation is simulated for hypersonic boost-glide vehicles. The simulation results demonstrate that the proposed algorithm can provide higher positioning accuracy than conventional geomagnetic matching algorithms. When the random error range is +/- 30 nT, the average absolute latitude error and longitude error of the MCAL algorithm are 151 m and 511 m lower, respectively, than those of the Sandia inertial magnetic aided navigation (SIMAN) algorithm.

Automated summary

By addressing the issue of divergence in geomagnetic matching navigation algorithms for hypersonic vehicles, a multi-geomagnetic-component assisted localization (MCAL) algorithm is proposed to improve positioning accuracy. By using intersections of geomagnetic components' isopleths and a decision method to estimate the real position of a carrier, the algorithm can provide higher positioning accuracy. Simulation results show that the MCAL algorithm outperforms conventional geomagnetic matching algorithms.