A New Geomagnetic Vector Navigation Method Based on a Two-Stage Neural Network

• Main Authors: Chen, D. (Author), Chen, Z. (Author), Liu, Z. (Author), Pan, M. (Author), Xu, Y. (Author), Zhang, Q. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: geomagnetic navigation; geomagnetic vector field; pattern recognition; two-stage neural network
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 20799292 (ISSN)
• DOI: 10.3390/electronics12091975

The traditional geomagnetic matching navigation method is based on the correlation criteria operations between measurement sequences and a geomagnetic map. However, when the gradient of the geomagnetic field is small, there are multiple similar data in the geomagnetic database to the measurement value, which means the correlation-based matching method fails. Based on the idea of pattern recognition, this paper constructs a two-stage neural network by cascading a probabilistic neural network and a non-fully connected neural network to, respectively, classify geomagnetic vectors and their feature information in two steps: “coarse screening” and “fine screening”. The effectiveness and accuracy of the geomagnetic vector navigation algorithm based on the two-stage neural network are verified through simulation and experiments. In simulation, it is verified that when the geomagnetic average gradient is 5 nT/km, the traditional geomagnetic matching method fails, while the positioning accuracy based on the proposed method is 40.17 m, and the matching success rate also reaches 98.13%. Further, in flight experiments, under an average gradient of 11 nT/km, the positioning error based on the proposed method is 39.01 m, and the matching success rate also reaches 99.42%. © 2023 by the authors.